#hardware acceleration just makes the video use your gpu to stream video instead of cpu

[clown-machine]

According to most of the notes it seems like whatever the capture blocking protocol is is dealt with using hardware acceleration in most browsers, so turning off hardware acceleration in the general/performance settings will let you capture again.

hbo max blocks screenshots even when I use the snipping tool AND firefox AND ublock which is a fucking first. i will never understand streaming services blocking the ability to take screenshots thats literally free advertising for your show right there. HOW THE HELL IS SOMEBODY GONNA PIRATE YOUR SHOW THROUGH SCREENSHOTS. JACKASS

[lowendbox]

In today’s tech landscape, the average VPS just doesn’t cut it for everyone. Whether you're a machine learning enthusiast, video editor, indie game developer, or just someone with a demanding workload, you've probably hit a wall with standard CPU-based servers. That’s where GPU-enabled VPS instances come in. A GPU VPS is a virtual server that includes access to a dedicated Graphics Processing Unit, like an NVIDIA RTX 3070, 4090, or even enterprise-grade cards like the A100 or H100. These are the same GPUs powering AI research labs, high-end gaming rigs, and advanced rendering farms. But thanks to the rise of affordable infrastructure providers, you don’t need to spend thousands to tap into that power. At LowEndBox, we’ve always been about helping users find the best hosting deals on a budget. Recently, we’ve extended that mission into the world of GPU servers. With our new Cheap GPU VPS Directory, you can now easily discover reliable, low-cost GPU hosting solutions for all kinds of high-performance tasks. So what exactly can you do with a GPU VPS? And why should you rent one instead of buying hardware? Let’s break it down. 1. AI & Machine Learning If you’re doing anything with artificial intelligence, machine learning, or deep learning, a GPU VPS is no longer optional, it’s essential. Modern AI models require enormous amounts of computation, particularly during training or fine-tuning. CPUs simply can’t keep up with the matrix-heavy math required for neural networks. That’s where GPUs shine. For example, if you’re experimenting with open-source Large Language Models (LLMs) like Mistral, LLaMA, Mixtral, or Falcon, you’ll need a GPU with sufficient VRAM just to load the model—let alone fine-tune it or run inference at scale. Even moderately sized models such as LLaMA 2–7B or Mistral 7B require GPUs with 16GB of VRAM or more, which many affordable LowEndBox-listed hosts now offer. Beyond language models, researchers and developers use GPU VPS instances for: Fine-tuning vision models (like YOLOv8 or CLIP) Running frameworks like PyTorch, TensorFlow, JAX, or Hugging Face Transformers Inference serving using APIs like vLLM or Text Generation WebUI Experimenting with LoRA (Low-Rank Adaptation) to fine-tune LLMs on smaller datasets The beauty of renting a GPU VPS through LowEndBox is that you get access to the raw horsepower of an NVIDIA GPU, like an RTX 3090, 4090, or A6000, without spending thousands upfront. Many of the providers in our Cheap GPU VPS Directory support modern drivers and Docker, making it easy to deploy open-source AI stacks quickly. Whether you’re running Stable Diffusion, building a custom chatbot with LLaMA 2, or just learning the ropes of AI development, a GPU-enabled VPS can help you train and deploy models faster, more efficiently, and more affordably. 2. Video Rendering & Content Creation GPU-enabled VPS instances aren’t just for coders and researchers, they’re a huge asset for video editors, 3D animators, and digital artists as well. Whether you're rendering animations in Blender, editing 4K video in DaVinci Resolve, or generating visual effects with Adobe After Effects, a capable GPU can drastically reduce render times and improve responsiveness. Using a remote GPU server also allows you to offload intensive rendering tasks, keeping your local machine free for creative work. Many users even set up a pipeline using tools like FFmpeg, HandBrake, or Nuke, orchestrating remote batch renders or encoding jobs from anywhere in the world. With LowEndBox’s curated Cheap GPU List, you can find hourly or monthly rentals that match your creative needs—without having to build out your own costly workstation. 3. Cloud Gaming & Game Server Hosting Cloud gaming is another space where GPU VPS hosting makes a serious impact. Want to stream a full Windows desktop with hardware-accelerated graphics? Need to host a private Minecraft, Valheim, or CS:GO server with mods and enhanced visuals? A GPU server gives you the headroom to do it smoothly. Some users even use GPU VPSs for game development, testing their builds in environments that simulate the hardware their end users will have. It’s also a smart way to experiment with virtualized game streaming platforms like Parsec or Moonlight, especially if you're developing a cloud gaming experience of your own. With options from providers like InterServer and Crunchbits on LowEndBox, setting up a GPU-powered game or dev server has never been easier or more affordable. 4. Cryptocurrency Mining While the crypto boom has cooled off, GPU mining is still very much alive for certain coins, especially those that resist ASIC centralization. Coins like Ethereum Classic, Ravencoin, or newer GPU-friendly tokens still attract miners looking to earn with minimal overhead. Renting a GPU VPS gives you a low-risk way to test your mining setup, compare hash rates, or try out different software like T-Rex, NBMiner, or TeamRedMiner, all without buying hardware upfront. It's a particularly useful approach for part-time miners, researchers, or developers working on blockchain infrastructure. And with LowEndBox’s flexible, budget-focused listings, you can find hourly or monthly GPU rentals that suit your experimentation budget perfectly. Why Rent a GPU VPS Through LowEndBox? ✅ Lower CostEnterprise GPU hosting can get pricey fast. We surface deals starting under $50/month—some even less. For example: Crunchbits offers RTX 3070s for around $65/month. InterServer lists setups with RTX 4090s, Ryzen CPUs, and 192GB RAM for just $399/month. TensorDock provides hourly options, with prices like $0.34/hr for RTX 4090s and $2.21/hr for H100s. Explore all your options on our Cheap GPU VPS Directory. ✅ No Hardware CommitmentRenting gives you flexibility. Whether you need GPU power for just a few hours or a couple of months, you don’t have to commit to hardware purchases—or worry about depreciation. ✅ Easy ScalabilityWhen your project grows, so can your resources. Many GPU VPS providers listed on LowEndBox offer flexible upgrade paths, allowing you to scale up without downtime. Start Exploring GPU VPS Deals Today Whether you’re training models, rendering video, mining crypto, or building GPU-powered apps, renting a GPU-enabled VPS can save you time and money. Start browsing the latest GPU deals on LowEndBox and get the computing power you need, without the sticker shock. We've included a couple links to useful lists below to help you make an informed VPS/GPU-enabled purchasing decision. https://lowendbox.com/cheap-gpu-list-nvidia-gpus-for-ai-training-llm-models-and-more/ https://lowendbox.com/best-cheap-vps-hosting-updated-2020/ https://lowendbox.com/blog/2-usd-vps-cheap-vps-under-2-month/ Read the full article

[lancecarr]

Streaming Media Guide to Building a Streaming Workstation

Streamingmedia.com recently posted a great article detailing everything you need to know about building your own streaming workstation. We’re going to be taking a look at some of the finer points of the article.

It takes a lot of know how, and research, as well as time and energy to create an efficient workstation. Choosing the right hardware is not only daunting- it is a make or break. This is why Telestream introduced their WireCast Gear Product. However, there will still be streamers and users who either require a custom work station- or want the experience of building one.

Generally speaking, live streams done at a higher production quality tend to be somewhat higher rated- and do a better job of retaining views. However, running a powerful real time software application requires a higher demand on your system.

One of the most important things about building your work station is making sure you have the basic workflow requirements for your stream.

First Things First

The horsepower of your machine is first and foremost. So when you are building your own workstation, there are four components that are end all be all.

CPU & RAM

Motherboard

GPU

Storage (for both OS and Media)

CPU and RAM

And underpowered CPU can be detrimental to your streaming capabilities. So what CPU do you need? That depends.

CPUs are differentiated by their clock speed as well as the number of processing cores they feature. In most instances users want to aim for a middle of the pack CPU when it comes to cores vs clock frequency. Its worth noting that all the latest mid-range Intel CPUs support eight cores.

“Theoretically, having a higher clock speed is always going to result in a better encode. When playing back video files, things can vary drastically based on the codec and the format of the source. For example, you can play out a 720p ProRes file and it’s going to impact the CPU far less than a more compressed version of the same material.”

streamingmedia.com

Motherboard

The motherboard of your computer is where everything flows in and out of. An improperly configured motherboard from a previous generation will not be able to deliver the speed and density of data streamers need to produce their content.

A lot of people focus on the actual speed of the bus, but that’s not the most important metric. It’s much more important to understand what components are sharing the bus, through which lanes, and how your chipset’s PCIe lanes are mapped out. 

streamingmedia.com

GPU

The GPU is also critical to your workflow- especially for professional streamers. GPU resources are precious and critical for the operation of your software. It is also vital for loading items into the UI and decoding H.264 videos.

Using the GPU to encode video streams instead of the CPU is a popular option. As discussed, CPU resources are precious. We need those CPU resources for the operation of the software, for loading things in the UI, to decode H.264 videos, and generally run all the processes that can’t be accelerated through the GPU. Using the CPU for the encoding workflow significantly reduces your overhead if things get backed up. Sometimes the smallest background task can throw a wrench into things. With GPU encoding, it’s a completely different processing unit so you’re not adding all that work onto your CPU’s to-do list. You end up with much more headroom and are far less likely to have an issue.

streamingmedia.com

Storage

Currently the debate between HDD and SDD drives is a hot topic- and NVMe solid state drives have just been added to the discussion. Needless to say- there are ample choices for storage available. Where Spinning Hard Disks give users a lot of storage at a low price- the technology tends to run very slow.

If your workflow involves multiple ProRes ISOs then you may want an SSD or a Solid State Drive (or multiple). This is because the system is reading and writing a massive amount of data.

You could use multiple drives, one for each encode, but you might run out of SATA ports on your motherboard. This is particularly problematic on laptops since they don’t typically have a lot of ports. SSDs have a fairly good price to storage ratio these days (1 TB for $110) and they offer a tremendous increase in speed. This allows you to do more things simultaneously without slowing down—more ISOs, more program recording.

streamingmedia.com

Check out the full article from Streamingmedia.com to find out more.

The post Streaming Media Guide to Building a Streaming Workstation appeared first on Videoguys Blog.

https://videoguys.com/blog/streaming-media-guide-to-building-a-streaming-workstation/

[sciencespies]

A glimpse into the future: Accelerated computing for accelerated particles

https://sciencespies.com/physics/a-glimpse-into-the-future-accelerated-computing-for-accelerated-particles/

A glimpse into the future: Accelerated computing for accelerated particles

Particles emerging from proton collisions at CERN’s Large Hadron Collider travel through through this stories-high, many-layered instrument, the CMS detector. In 2026, the LHC will produce 20 times the data it does currently, and CMS is currently undergoing upgrades to read and process the data deluge. Credit: Maximilien Brice, CERN

Every proton collision at the Large Hadron Collider is different, but only a few are special. The special collisions generate particles in unusual patterns—possible manifestations of new, rule-breaking physics—or help fill in our incomplete picture of the universe.

Finding these collisions is harder than the proverbial search for the needle in the haystack. But game-changing help is on the way. Fermilab scientists and other collaborators successfully tested a prototype machine-learning technology that speeds up processing by 30 to 175 times compared to traditional methods.

Confronting 40 million collisions every second, scientists at the LHC use powerful, nimble computers to pluck the gems—whether it’s a Higgs particle or hints of dark matter—from the vast static of ordinary collisions.

Rifling through simulated LHC collision data, the machine learning technology successfully learned to identify a particular postcollision pattern—a particular spray of particles flying through a detector—as it flipped through an astonishing 600 images per second. Traditional methods process less than one image per second.

The technology could even be offered as a service on external computers. Using this offloading model would allow researchers to analyze more data more quickly and leave more LHC computing space available to do other work.

It is a promising glimpse into how machine learning services are supporting a field in which already enormous amounts of data are only going to get bigger.

The challenge: more data, more computing power

Researchers are currently upgrading the LHC to smash protons at five times its current rate. By 2026, the 17-mile circular underground machine at the European laboratory CERN will produce 20 times more data than it does now.

CMS is one of the particle detectors at the Large Hadron Collider, and CMS collaborators are in the midst of some upgrades of their own, enabling the intricate, stories-high instrument to take more sophisticated pictures of the LHC’s particle collisions. Fermilab is the lead U.S. laboratory for the CMS experiment.

If LHC scientists wanted to save all the raw collision data they’d collect in a year from the High-Luminosity LHC, they’d have to find a way to store about 1 exabyte (about 1 trillion personal external hard drives), of which only a sliver may unveil new phenomena. LHC computers are programmed to select this tiny fraction, making split-second decisions about which data is valuable enough to be sent downstream for further study.

Currently, the LHC’s computing system keeps roughly one in every 100,000 particle events. But current storage protocols won’t be able to keep up with the future data flood, which will accumulate over decades of data taking. And the higher-resolution pictures captured by the upgraded CMS detector won’t make the job any easier. It all translates into a need for more than 10 times the computing resources than the LHC has now.

Particle physicists are exploring the use of computers with machine learning capabilities for processing images of particle collisions at CMS, teaching them to rapidly identify various collision patterns. Credit: Eamonn Maguire/Antarctic Design

The recent prototype test shows that, with advances in machine learning and computing hardware, researchers expect to be able to winnow the data emerging from the upcoming High-Luminosity LHC when it comes online.

“The hope here is that you can do very sophisticated things with machine learning and also do them faster,” said Nhan Tran, a Fermilab scientist on the CMS experiment and one of the leads on the recent test. “This is important, since our data will get more and more complex with upgraded detectors and busier collision environments.”

Machine learning to the rescue: the inference difference

Machine learning in particle physics isn’t new. Physicists use machine learning for every stage of data processing in a collider experiment.

But with machine learning technology that can chew through LHC data up to 175 times faster than traditional methods, particle physicists are ascending a game-changing step on the collision-computation course.

The rapid rates are thanks to cleverly engineered hardware in the platform, Microsoft’s Azure ML, which speeds up a process called inference.

To understand inference, consider an algorithm that’s been trained to recognize the image of a motorcycle: The object has two wheels and two handles that are attached to a larger metal body. The algorithm is smart enough to know that a wheelbarrow, which has similar attributes, is not a motorcycle. As the system scans new images of other two-wheeled, two-handled objects, it predicts—or infers—which are motorcycles. And as the algorithm’s prediction errors are corrected, it becomes pretty deft at identifying them. A billion scans later, it’s on its inference game.

Most machine learning platforms are built to understand how to classify images, but not physics-specific images. Physicists have to teach them the physics part, such as recognizing tracks created by the Higgs boson or searching for hints of dark matter.

Researchers at Fermilab, CERN, MIT, the University of Washington and other collaborators trained Azure ML to identify pictures of top quarks—a short-lived elementary particle that is about 180 times heavier than a proton—from simulated CMS data. Specifically, Azure was to look for images of top quark jets, clouds of particles pulled out of the vacuum by a single top quark zinging away from the collision.

“We sent it the images, training it on physics data,” said Fermilab scientist Burt Holzman, a lead on the project. “And it exhibited state-of-the-art performance. It was very fast. That means we can pipeline a large number of these things. In general, these techniques are pretty good.”

One of the techniques behind inference acceleration is to combine traditional with specialized processors, a marriage known as heterogeneous computing architecture.

Data from particle physics experiments are stored on computing farms like this one, the Grid Computing Center at Fermilab. Outside organizations offer their computing farms as a service to particle physics experiments, making more space available on the experiments’ servers. Credit: Reidar Hahn

Different platforms use different architectures. The traditional processors are CPUs (central processing units). The best known specialized processors are GPUs (graphics processing units) and FPGAs (field programmable gate arrays). Azure ML combines CPUs and FPGAs.

“The reason that these processes need to be accelerated is that these are big computations. You’re talking about 25 billion operations,” Tran said. “Fitting that onto an FPGA, mapping that on, and doing it in a reasonable amount of time is a real achievement.”

And it’s starting to be offered as a service, too. The test was the first time anyone has demonstrated how this kind of heterogeneous, as-a-service architecture can be used for fundamental physics.

In the computing world, using something “as a service” has a specific meaning. An outside organization provides resources—machine learning or hardware—as a service, and users—scientists—draw on those resources when needed. It’s similar to how your video streaming company provides hours of binge-watching TV as a service. You don’t need to own your own DVDs and DVD player. You use their library and interface instead.

Data from the Large Hadron Collider is typically stored and processed on computer servers at CERN and partner institutions such as Fermilab. With machine learning offered up as easily as any other web service might be, intensive computations can be carried out anywhere the service is offered—including off site. This bolsters the labs’ capabilities with additional computing power and resources while sparing them from having to furnish their own servers.

“The idea of doing accelerated computing has been around decades, but the traditional model was to buy a computer cluster with GPUs and install it locally at the lab,” Holzman said. “The idea of offloading the work to a farm off site with specialized hardware, providing machine learning as a service—that worked as advertised.”

The Azure ML farm is in Virginia. It takes only 100 milliseconds for computers at Fermilab near Chicago, Illinois, to send an image of a particle event to the Azure cloud, process it, and return it. That’s a 2,500-kilometer, data-dense trip in the blink of an eye.

“The plumbing that goes with all of that is another achievement,” Tran said. “The concept of abstracting that data as a thing you just send somewhere else, and it just comes back, was the most pleasantly surprising thing about this project. We don’t have to replace everything in our own computing center with a whole bunch of new stuff. We keep all of it, send the hard computations off and get it to come back later.”

Scientists look forward to scaling the technology to tackle other big-data challenges at the LHC. They also plan to test other platforms, such as Amazon AWS, Google Cloud and IBM Cloud, as they explore what else can be accomplished through machine learning, which has seen rapid evolution over the past few years.

“The models that were state-of-the-art for 2015 are standard today,” Tran said.

As a tool, machine learning continues to give particle physics new ways of glimpsing the universe. It’s also impressive in its own right.

“That we can take something that’s trained to discriminate between pictures of animals and people, do some modest amount computation, and have it tell me the difference between a top quark jet and background?” Holzman said. “That’s something that blows my mind.”

Explore further

Higgs boson machine-learning challenge

Provided by Fermi National Accelerator Laboratory

Citation: A glimpse into the future: Accelerated computing for accelerated particles (2019, August 16) retrieved 16 August 2019 from https://phys.org/news/2019-08-glimpse-future-particles.html

This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.

#Physics

[gravitylink-blog]

Who’s Better: Google Coral USB Accelerator and Intel 2nd Generation Neural Compute Stick?

As artificial intelligence (AI) and machine learning (ML) gradually move from science fiction to real life, we now need a fast and convenient way to prototype this type of system. Although desktop computers can also be sufficient to meet the operating requirements of AI/ML, even single-board computers such as Raspberry Pi can meet these requirements. But what if you just want a simple plug-in device to make your system run faster and more powerful?

Don't worry, you actually have a variety of choices, including Google Coral Edge TPU series hardware USB Accelerator (Coral USB accelerator, hereinafter referred to as CUA) and Intel's Neural Compute Stick 2 (NCS2). Both devices are computing devices plugged into the host via USB. NCS2 uses a visual processing unit (VPU), while Coral USB Accelerator uses a tensor processing unit (TPU), both of which are dedicated processing devices for machine learning. Today I will give you a test and comparison: What is the difference between the two? As a developer, should you choose Coral or NCS2? Please check below.

Coral USB Accelerator

-ML accelerator: Edge TPU ASIC (application specific integrated circuit) chip designed by Google. It provides high-performance ML inference for TensorFlow Lite models (MobileNet V2 400 + fps, from the latest official updated data).

-Support USB 3.1 port and cable (SuperSpeed, 5GB/s transfer speed)

-Dimensions: 30 x 65 x 8 mm

-Official price: $74.99

Intel Neural Compute Stick 2

-Processor: Intel Movidius Myriad X Visual Processing Unit (VPU)

-USB 3.0 Type-A

-Size: 72.5 x 27 x 14mm

-Official price: $87.99

1. Comparison of processor and acceleration performance

Unlike the way you compare traditional computer CPUs, the details of comparing each processor/accelerator are more subtle, depending on how you plan to use them. Although the output format is slightly different (every inference time and the number of frames per second), we can still compare the two devices in some overall performance modes.

When evaluating AI models and hardware platforms for real-time deployment, the first thing to look at is their speed. In computer vision tasks, benchmarks are usually measured in frames per second (FPS). Higher numbers indicate better performance. For real-time video streaming, at least about 10 fps is required to make the video appear smooth.

Operational performance: First, when CUA is added to the desktop CPU, the performance can be increased by about 10 times, and the operation performance is relatively good. (Depending on the selected CPU model, the 10x performance fluctuates slightly) NCS2 "cooperates" with the older Atom processor, which can increase the processing speed by nearly 7 times. However, when paired with a more powerful processor, the results presented by NCS2 are not surprising.

NCS2 can theoretically perform inference at a speed of 4 TOPS. Curiously, CUA also has the exact same rate, although both use different operations to perform ML. In addition, Intel claims that the performance of NCS2 is 8 times that of the original neural computing stick. (If you like, you can choose NCS2 instead of the original neural computing stick, although the price is lower.)

NCS2 can use MobileNet-v2 to run the 30 FPS classification model, which is not bad. However, object detection at 11 FPS is a bit difficult. A frame rate of about 10 FPS may not be sufficient for real-time object tracking, especially for high-speed motion, and many objects may be lost, and developers need very good tracking algorithms to make up for this "hole". (Of course, the official benchmark results are not completely credible. Often, these companies compare their manually optimized software with competitors’ out-of-the-box models.)

User evaluation

Power consumption: NCS2 has lower power consumption. As far as CUA is concerned, the official does list 0.5 watts for each TOPS. Users can also set CUA to the default speed or maximum (2 times the default value) as needed.

It is worth noting that Google’s official documents do clearly remind: the power transmission when the device is running at maximum speed and the maximum ambient temperature may burn your skin. So personally, unless you really need additional processing power, it is best to run it in normal mode.

It is also important to remember that Python is not the first choice for good performance on devices. Both of these devices support the C++ API, which is also my "trick to get the best performance from the device in my tests."

2. Software support

NCS2 can be used with Ubuntu, CentOS, Windows 10 and other operating systems. It can support TensorFlow, Caffe, ApacheMXNet, and PyTorch and PaddlePadle through open neural network conversion.

CUA does not support Windows, but it can run under Debian 6.0 or higher (or any derivative version, such as Ubuntu 10.0+). It is worth mentioning that CUA can only officially run TensorFlow Lite models.

3. Comparison of size, prototype design and other details

After covering the software support, computing power and power consumption, what is the specific situation of the two in the actual construction of product prototypes?

Frankly speaking, both devices look very cool. The CUA is a slightly silver-white checkered body with a partially transparent body and what appears to be a heat sink. The NCS2 is a sleek blue design, and the blue body and integrated radiator look more fashionable.

Of course, appearance is only secondary. The important thing is that NCS2 does, like CUA, will become hot during operation. However, its radiator design allows you to hold it on a cooler integrated heat sink without having to hold it with your fingers in the middle, which is very clever.

The design of NCS2 allows users to use multiple computing sticks together to enhance its processing capabilities. You can arrange them neatly in a vertical USB docking station. Similarly, a host can run multiple CUAs, but you may need to find another way to save each CUA. It is worth mentioning that although both have similar dimensions, the thickness of NCS2 (14 mm) is almost twice that of CUA. In addition, it is inserted through a USB plug (such as an extra large thumb drive) instead of a flexible cable like CUA, which means that in some operating scenarios, NCS2 will make you very difficult to deal with space issues. difficult. You have to use data cables and docking stations extensively. This is something you need to consider before you make a choice.

Finally, NCS2 and CUA seem to be dedicated devices designed for edge computing applications. If you need to run on a Windows system or need to run outside of the Tensorflow Lite framework, then NCS2 has obvious advantages. For its part, Coral USB Accelerator's peripheral supporting hardware, there are more simple and rude development board Dev Board, PCI accelerator designed with Coral Edge TPU as the core, and SoM module similar to the development board, etc. If your need is to quickly bring product prototyping to the market, then Coral is your best choice, and it is more attractive to developers.

Coral USB Accelerator development environment requirements: a Linux computer with a USB port; support Debian 6.0 or higher, or its derivative systems (such as Ubuntu 10.0+); x86_64 or ARM64 system architecture with ARMv8 instruction set.

Therefore, from the above requirements, Coral USB accelerator supports Raspberry Pi. However, it must be Raspberry Pi 2/3 Model B/B+ and run Raspbian system (or other Debian derivative systems).

At this point, the functions between the two are very similar. If you want to add AI/ML to Raspberry Pi or similar projects, both devices can work normally.

Many pre-compiled network models allow you to get better results easily and quickly. Nevertheless, fully quantifying your network is still an advanced task. Conversion requires an in-depth understanding of the network and how it operates. In addition, when I upgraded from FP_32 to FP_16 and from FP_16 to UINT, the loss of accuracy was also great. Interestingly, Myriad can handle half of its floating point, and CUA can only handle 8-bit floating point. This means that Myriad can obtain higher accuracy.

Intel and Google obviously adopted two completely different "routines. Google's advantage is that products can help developers easily build prototypes and promote a complete set of solutions from Google Cloud Platform to edge-tpu. I personally like how all the components are Work together. On the other hand, Intel provides Openvino plug-ins, developers can use them to optimize their network, so that it can run on a variety of hardware. OpenVINO currently supports Intel CPU, GPU, FPGA and VPU. At Intel The challenge ahead is that these "combined punches" are always difficult to use the optimal function of each component.

Google Coral USB Accelerator can train network models online, which is essential for migration learning. Obviously, Google believes that their pre-training network and migration learning provide developers with an efficient combination. In addition, Intel NCS2 has three pairs of built-in stereo depth hardware, which are valuable in many use cases (such as obstacle avoidance).

Application scenario:

Intel NCS2 also provides prototype, verification and deployment of DNN. For driverless and driverless vehicles and IoT devices, low power consumption is essential. For those who wish to develop deep learning inference applications, NCS2 is one of the most energy-efficient and lowest-cost USB sticks.

For example, the upcoming Titanium AIX also has built-in Intel Movidius Myraid X computing acceleration chip, which lowers the threshold of AI learning and development, and helps AI enthusiasts and developers quickly build AI applications and solutions that can listen, talk, and watch.

Google Coral is more than just hardware. It easily combines the functions of custom hardware, open software and advanced AI algorithms and provides high-quality AI solutions. Coral has many application cases in helping industrial development, including predictive maintenance, anomaly detection, robotics, machine vision, and voice recognition. It has great application value in manufacturing, healthcare, retail, smart space, internal monitoring and transportation sectors.

[cryptoandblockchaintalk]

EPISODE 13: Blockchains and Algorithms

  Blockchains and Algorithms

  Blockchain is the latest tech craze and has attracted a lot of attention. Before blockchain, computers operated in a client/server relationship, where one central authority (the server) is contacted by a client (your laptop, phone, etc.) and is requested to send back information. An example of this is when you use Netflix. Your TV connects to a Netflix server, which has preloaded movies and shows for you to choose from. Once you select your show, it then streams the video content to you. That’s fine, until Netflix removes the show you want to watch, and you let out a big sigh. Well, it could be that their contract for sharing that show expired, or they needed the space to upload a new show. Either way, it is too bad for you.

  What if there was infinite space, lots of bandwidth, anyone could upload a movie or TV show, and nobody was in charge, so your favorite show could never get removed? Sounds too good to be true, right? Well, that’s essentially what blockchain is: A network of ordinary computers, sharing resources, all working together with no master. The only control is the code shared by all these computers, which make the blockchain.

  Every blockchain (network of computers) is designed to do something different. There are video distribution service blockchains being created right now, but there are many different uses for this technology, and ways to go about creating this network. No one way is perfect right now, and there is a huge race to evolve into the best overall solution. To have a blockchain, computers called miners must create new blocks which are then accepted or rejected by the network. Blocks are rejected if they are invalid, malicious, or incomplete. There have to be certain rules and guidelines set in place in order for the network to function properly. It wouldn’t make sense if a Netflix blockchain suddenly allowed someone to upload a book, or even a podcast, or upload a movie claiming themselves as the director or producer.

  Here’s a brief overview of the Top 10 most used algorithms, and a sample of which popular blockchains use them:

  Proof of Work - Bitcoin, Ethereum, and Monero Bitcoin’s founder created Proof of Work, where miners configure their computers to solve very intense puzzles, competing to create the next block. When they solve that puzzle (once every 10 minutes), they are given a reward (coins). Bitcoin has become so valuable, entire countries are setting up huge factories to create blocks and claim rewards. Places that have free power have a tremendous advantage, as the machines required to mine these blocks suck up unbelievable amounts of power. A decommissioned power plant in Australia was recently purchased solely for the purpose of Bitcoin mining. Video cards from NVIDIA and AMD have also been in exceptional demand. They are selling above MSRP, often sold out the same day a retailer has them in stock.

Proof of Stake - NEO, DASH, and NavCoin Instead of mining, people who hold the coins belonging to the network have the option of being “minters”. A designated third party creates the blocks, then the minters run software on their computers that act as validators. It’s much less energy intensive and requires no special hardware, just coins. The more coins you have “staked” in the blockchain, the higher the chance you have at being selected (at random) to validate a block.

Delegated Proof of Stake - EOS, Bitshares, and Lisk There is a major difference in this system compared to the previous two listed. Instead of everybody having a chance at validating a block, only a small number of machines on the network are selected, by a vote. This way, it is much easier for a handful of computers to talk to one another to compare blocks and validation, in a much more efficient way. If one validator starts to fail, they are voted out of circulation and replaced by another computer on the network. Instead of miners competing with each other, in DPoS they all work together to solve the block puzzle, creating blocks much, much faster.

Proof of Authority - POA Network, Ethereum’s Kovan Testnet This consensus algorithm is pretty much the same as the client/server relationship. There is one authority in charge of everything, and the clients can just read the information (if allowed). This is best used in private corporate chains. Additional conditions to become an authority can be specified, including identity management and verification, in contrast to the previously mentioned algorithms, where the only identity on the network is often a wallet address or IP address/port.

Proof of Activity - Decred This is a combination of both PoW and PoS. Currently only Decred uses it. Pros and Cons remain to be seen.

Proof of Burn - Binance Coin, Counterparty, Slimcoin Instead of being rewarded for solving puzzles, you are supposed to send coins to an irretrievable address, to be lost forever. The concept is by decreasing the supply of coins, the value of the remaining ones increase. More often than not, the practice of “burning” coins is something done by the developers rather than the community. Blockchain ICOs often burn any unsold tokens remaining, as they cannot be allocated to any other area in an honest way. Binance burns some of its profits every quarter, to ensure its coin gains value.

Proof of Elapsed Time - Hyperledger Sawtooth Every participant in the network is assigned a random time in each block. Whoever’s time runs out first, gets to create the next block. Clients on this network must be identified, and their code must have passed verification trust, to avoid simply exploiting the time on your network node to trigger sooner. Pros and cons are yet to be determined.

Directed Acyclic Graph - IOTA, Hashgraph, and Nano Participants in this network that want to send a transaction, are first required to do a small proof of work, in which they verify two previous transactions that occurred before. In theory, this tangle can accelerate to near-instant speeds. Unlike traditional servers, the more users that are logged on, it gets faster instead of slower. Each of the networks has their own unique take on how to organize transactions, so read into each project more if you desire.

Byzantine Fault Tolerance - Ripple, Stellar, and Dispatch Pre-selected validators are in charge of the network. No miners or community contributors are required. With that said, Stellar allows anyone to become a validator, while Ripple itself decides who validates blocks on their network. These are centralized services using blockchain technology.

Proof of Space/Capacity - Burst

Although, only one coin currently uses this, it is a fantastic algorithm. It allows people to mine using spare hard drive space, instead of CPU/GPU cycles. Your hard drive space is filled up with small amounts of random files that act like lottery tickets. The network elects a winner every defined number of seconds (or minutes) and gives them a coin reward. It takes tiny fractions of energy (5 watts) to power a hard drive, compared to 100+ watts of a single GPU or 1200+ watts of an ASIC (specialized mining hardware developed just for Bitcoin and other major coins).

  There are still many more algorithms out there, including:

  Proof of Importance

Proof of Reputation

Proof of Weight

Proof of Evolution

Tendermint

CASPER

Distributed Byzantine Fault Tolerance

SPECTRE

  If you’re thinking of creating a blockchain of your own, continue your research! It will be time well spent.

Listen to this episode of Crypto and Blockchain Talk

                                                                   Check out this episode!

[the-fitsquad]

Workstation NEW

Workstation W1110 Assistance

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Is only a shade slower than the Intel Xeon E3-1270 v6 (three.8GHz up to 4.2GHz), the quickest model obtainable in the HP Z240 SFF. The desk’s surface is exactly where you’ll discover a control panel for adjusting the height of the standing platform when you get it set to the height you wish, you can save that setting and easily adjust amongst the seated and standing positions. This indicates that it has an equivalent possible of 25 GHz processing power per processor, which is much more than 3.64 = 14.four GHz of the i7 3820. You can generate and edit DWG files swiftly and, now that AutoCAD is accessible on Mac, operate across platforms as well. Note that this guidance explicitly differentiates amongst requiring access to distinct services on the internet (such as Azure and Workplace 365 administrative portals) and the “Open Internet” of all hosts and solutions.

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[davidegbert]

Intel Core i7-7700K 'Kaby Lake' and Asus Maximus IX Hero Review

Intel is in somewhat of a tight spot. The industry demands new products on a yearly cycle, but as far as processors go, there isn't a whole lot more that PC and laptop buyers today need from their machines that they can't get from the ones they already have. Sure, everyone would like a lighter laptop and more power overall at the same price, but let's face it, there's no new killer app or usage scenario that demands a leap in performance, and there hasn't been for a long time.

The world's largest PC chipmaker can keep pushing transistor sizes down, squeezing more life out of each jump to a smaller manufacturing process, but the process gets more expensive and complicated with each generation. We're also facing the prospect of diminishing returns with each such transition.

Well over a year ago, Intel announced that it would break its longstanding practice of introducing a smaller process every other release cycle, and instead ship a third generation of products on the then-new 14nm node. A new codename, Kaby Lake, was inserted into roadmaps to serve as a successor to Skylake and fill the void created by pushing the 10nm Cannonlake generation a year out.

Much like all of Intel's releases, Kaby Lake is coming out in phases. The first wave, in late 2016, comprised of the low-power U-series and Y-series processors for Ultrabooks, 2-in-1s and similar small devices, since these are the hottest selling PC form factors. Manufacturers were thereby able to release new products in time for the busy US holiday shopping season. Now, the same architecture is coming to servers, desktops and more traditional laptops, and we have the top-end enthusiast desktop model, the new Core i7-7700K, with us for review already.

There's also a new generation of platform controllers, aka chipsets, to go with Kaby Lake. We received a shiny new Z270-based Asus Maximus IX Hero to test our CPU with, and we'll be taking a good look at it as well.

Intel Core i7-7700K architecture and features Skylake was the first implementation of a new architecture, and Cannonlake was supposed to be the process shrink that followed. Now, we have an interloper, Kaby Lake - Intel is calling it the "optimisation" stage in a new three-year "process-architecture-optimisation" cycle. As such, there are few changes compared to Skylake. Intel is going so far as to say that Kaby Lake uses a "14nm+" manufacturing process, by which it means that performance gains can be realised thanks to improvements to the fabrication process alone. At the time of the U- and Y-series launch, Intel said that there would be a 12 percent increase in performance even if nothing else about the CPU itself was to change.

The Core i7-7700K in particular sits at the top of the list of offerings, and has a base speed of 4.2GHz with a maximum boost speed of 4.5GHz. The K indicates an unlocked multiplier for overclockers to tinker with. This is still a quad-core CPU with Hyper-Threading, as there are no major changes to the structure of the product lineup: all desktop Core i7s still have four cores and eight threads; Core i5 CPUs have four cores but no Hyper-Threading, and Core i3s have two cores with Hyper-Threading.

Still, there are some new things to talk about, most notably some major improvements to the integrated graphics capabilities of these processors. Desktop Kaby Lake CPUs will feature the new HD Graphics 630 integrated GPU which promises superior 4K video handling, including playback and encoding for the H.264, H.265, VP9, and HEVC standards at high bitrates.

Specifically, hardware acceleration for 4K HEVC 10-bit encode/decode and VP9 decode are new with this generation. 4K 60fps video can be decoded at up to 120Mbps, or if the quality is a more standard 4K 30fps, up to eight streams can be decoded simultaneously. There's also now support for HDR video tone mapping with a wide color gamut in accordance with the Rec.2020 specification which is part of the HDMI 2.0 standard and widely recognised in the broadcast industry.

Intel points out that Netflix and YouTube are aggressively pushing newer video standards in order to boost compression and reduce Internet bandwidth consumption, and that video is expected to account for 70 percent of all Internet traffic in 2017. This means there is a real appetite out there for such capabilities even in products aimed at consumers, not just content creators and professionals. People might not know the specifics of the standards, but "smoother and more efficient video streaming" is a highly relatable selling point.

Other than graphics, there are improvements to Intel's Speed Shift algorithms which govern when and how quickly the CPU adjusts its clock speed in response to changing workloads, with the goal of reducing power draw by either getting tasks over with quickly with a burst of speed, or slowing down to keep consumption below a point. Short bursts, in the range of milliseconds each, can help a PC feel more responsive.

Unsurprisingly for a stopgap product, there isn't really anything new to the CPU architecture itself. Intel is targeting people who use PCs and laptops which are now around five years old, and sure, users will see significant cumulative improvements if they haven't upgraded in that long. Kaby Lake doesn't push any new killer app or use-case scenario, but it will allow manufacturers to sell new PCs with newer, higher numbers on their spec sheets.

The 2XX platform On the other hand, a lot has changed outside of the CPU, and so Intel is releasing an updated series of platform controllers, known colloquially as chipsets. Kaby Lake uses exactly the same socket as Skylake, and there is full cross-compatibility between both lines of CPUs and motherboards (with a BIOS update). You don't strictly have to match generations, but there's always more benefit in going with a newer motherboard.

The 2XX series will directly replace the 1XX series, and has almost exactly the same tiers: Z270 at the top end for enthusiasts and overclockers, H270 for mainstream needs, plus Q2XX and B2XX for businesses with remote management and mass deployment needs. Only the low-end H110 for home and casual users will not be replaced with a H210, for unknown reasons.

There's more PCIe 3.0 bandwidth now, with a maximum of 24 on the Z270, up from 20, and 20 on the H270, up from 16. That means more motherboards will feature standards such as USB 3.1 Gen 2 (10GBps) and Thunderbolt 3 (up to 40Gbps). Z270 boards will also be able to officially work with DDR4-2400 RAM, up from DDR4-2133, as well as Intel's upcoming Optane lineup of high-speed storage and memory products. Everything else, from the number of SATA and USB 3.0 ports to the other standards supported, remains unchanged.

The Asus Maximus IX Hero Every motherboard manufacturer is riding this wave and releasing dozens upon dozens of new models. The one we have with us for our review is the Asus Maximus IX Hero, a Z270-based enthusiast model with a price tag of around Rs. 23,350. It's aimed squarely at overclockers who like to show off, but there's plenty of appeal even for less adventurous enthusiasts.

The board looks absolutely gorgeous, with a dark grey finish and subtle red accents. The heatsinks and port shroud have a sculpted angular look. It has a printed pattern, like most high-end boards these days, and thankfully it isn't garish at all. The layout is bog standard, with generous amounts of room around the CPU cooler.

Asus is pushing its Aura RGB lighting feature across its product line, and it's actually implemented really well on this new board. There are LEDs only around the PCH heatsink and port shroud, but they shine through various gaps and facets, looking great. There's a strip of LEDs aimed right where a CPU cooler's fan should be, lighting it up like a monument. If that isn't enough, you can connect standard LED strips to two headers on the motherboard and sync up your whole case. Of course, Asus hopes you buy one of its graphics cards and some more accessories too.

One very interesting touch is the abandonment of the much-unloved SATA Express standard. This is no loss at all, considering SATA Express SSDs never hit the market. The standard 6GBps SATA ports are complemented by two M.2 slots, one of which can accommodate extra-long modules up to 110mm and also supports SATA modules. The other one goes up to the standard 80mm and can only take PCIe modules. Both are raised, which we hope helps with cooling. There's no server-grade U.2, like we've seen on higher-end boards.

The rear port cluster has quite a lot going on. From left to right, there are buttons with which you can reset or update the BIOS without reaching inside your PC's case, then two Wi-Fi antenna terminals, DisplayPort and HDMI video outputs, four USB 2.0 ports, four USB 3.0 ports, Gigabit Ethernet, USB 3.1 Type-A and Type-C ports, five reassignable analog audio jacks, and digital S/PDIF.

The Maximus IX Hero features an ALC S1220 codec, ESS Sabre DAC and dedicated circuitry for its onboard sound. Asus of course lists a long line of high-quality components including capacitors, chokes, and regulators for the 8+2+2 phase power design.

For overclockers, there are power and monitoring terminals for water pumps and high-powered fans. There's even a debugging boot mode for those using liquid Nitrogen. We like Asus' numeric Q-code diagnostic readout, which helps diagnose boot-related issues. One final interesting touch is a new header for front-panel USB 3.1 ports, presumably including Type-C.

All in all, Asus has managed to make even an incremental update feel fresh, and if this one board is any indication, there will be a lot to like about the entire lineup.

Intel Core i7-7700K and Asus Maximus IX Hero performance: We tested Intel's new CPU on a matching motherboard, and compared scores to those of our Core i7-6700K. Integrated GPUs were used throughout. The specifications are as follows:

 SkylakeKaby LakeCPUIntel Core i7-6700KIntel Core i7-7700KMotherboardGigabyte Z170X-Gaming 7Asus Maximus IX HeroRAM2x8 GB Kingston HyperX DDR4-2666SSD256GB Samsung SSD 950 Pro CPU coolerCooler Master Hyper 212X PSUCorsair RM650 MonitorAsus PB287Q OSWindows 10 

As expected, there were no issues getting our test system up and running. We ran a variety of tests designed to challenge the CPU as well as GPU capabilities of the new platform, beginning with with standard synthetic tests, and then moving on to some light gaming and then real-world usage.

 Core i7-6700KCore i7-7700KCinebench R15 CPU multi-threaded930987Cinebench R15 CPU single-threaded182193Cinebench R15 OpenGL57.5fps61.23fpsPOVRay*2m, 9s2m, 0sWebXPRT338417Basemark Web 3.0398.79494.64PCMark 8 Home35714010PCMark 8 Creative34344055PCMark 8 Work458349433DMark Fire Strike Ultra2832973DMark Fire Strike117912453DMark Time Spy443458HyperPi11.938s11.141sSiSoft SANDRA CPU arithmetic140.86GOPS152.45GOPSSiSoft SANDRA CPU multimedia426.08Mpix/s458.52Mpix/sSiSoft SANDRA CPU encryption bandwidth9.76GBps10.1GBpsSiSoft SANDRA CPU performance/Watt1404.04MOPS/W1323MOPS/WSiSoft SANDRA cache bandwidth1404.04MOPS/W1323MOPS/WHandbrake video encoding*2:041:017zip file compression*2:302:26Rise of the Tomb Raider, 1920x1080, Low11.59fps12.46fpsStar Swarm6.15fps8.01fpsUnigine Valley, 1920x1080, Medium19.2fps20.9fps*lower is better

The one highlight of our report card is the Handbrake video encoding test. We transcoded a 1.36GB 1080p AVI file to 720p30 H.265 MKV for this test specifically to challenge the new CPU. The i7-7700K managed to finish its workload in just under half the time taken by the Skylake CPU, which is pretty impressive. This does have implications for home users as well as content creators. We just wish that overall performance had been improved to this degree.

We did try some light overclocking using Asus' bundled Dual Intelligent Processors 5 tool, which pushes speeds incrementally and performs stress tests at each stage, to verify that the system can run stably. Our sample CPU could be safely pushed to 4.8GHz using air cooling. However, when we ran our tests again, monitoring tools showed the CPU staying at 4.66GHz with only minor spikes above this level. Scores were not different enough for us to measure any significant improvement.

As you can see by the results, the new Core i7-7700K does outperform its predecessor consistently, but only by small margins except in very specific scenarios. Given that pretty much the entire Kaby Lake generation is a drop-in replacement for Skylake, this is not a bad thing at all. If you're about to buy or build a new PC and prices are the same, simply buy the new instead of the old. On the other hand, this launch doesn't give anyone a reason to rush out and upgrade.

Verdict While the larger buying public is unaware of Intel's behind-the-scenes roadmap changes and process technology delays, enthusiasts worry about things like whether Kaby Lake is a pointless stopgap created solely to keep a billion-dollar marketing machine running. The answer to that is while it might be true, customers are still getting slightly better performance at each level than they would have if Intel had simply prolonged the lifespan of the 6th generation. Plus, all process improvements will benefit future generations of products.

Our Core i7-7700K is a solid performer with reasonably low power consumption, and will not require massive, noisy cooling solutions, and we expect the rest of the lineup to be just as good. It might take a while for pricing to settle, especially in India, so if you find a Skylake equivalent part at a significantly lower price, you can go for it without feeling like you're losing out on anything.

The new GPU is of course welcome, but is more likely to be attractive to those who aren't going to pop in a much beefier graphics card, which means that desktop gamers and enthusiasts gain the least. Laptop buyers, on the other hand, would do well to look for the specific "7th Gen" wording on Intel's stickers - not only does graphics horsepower improve, but also battery life.

We're much more interested in the new flood of 2XX-series motherboards from Asus, Gigabyte, MSI, ASRock and others that will soon arrive in the market. More modern connectivity is always a good thing, and we can easily see ourselves recommending these boards over previous ones even for Skylake CPUs. The Asus Maximux IX Hero is probably overkill for many, but we were very pleased with it and would recommend it without hesitation.

For the past few years, reviews of high-end Intel processors have only made passing mention of AMD, the only other company in the x86 CPU race, for the simple reason that AMD has not had any product to offer that would even remotely be viable as a competitor. That could change in a dramatic way when Ryzen, its first high-performance lineup in years, is launched just a short while from now. We don't have solid information yet, but we're hoping for a fierce fight to reinvigorate this stagnant market.

Finally, if recent rumours and leaks are to be believed, 10nm is delayed even further there will be a fourth 14nm generation codenamed Coffeelake next year. With very little left in the way of fresh ideas, Intel is supposedly going to make six-core desktop models mainstream. Between Ryzen and this potential development, it sounds like there's enough reason to wait a little while before upgrading - or skip Kaby Lake entirely.

Intel Core i7-7700K Price: Rs. 32,700

Pros

Reasonable improvement over the Core i7-6700K at the same price

New HD and 4K video acceleration capabilities

Backwards compatible with Skylake motherboards 

Cons

Video benefits will be lost on anyone who uses a discrete GPU

Not the huge performance leap we were hoping for

Ratings (Out of 5)

Performance: 4.5

Value for Money: 4

Overall: 4

Asus Maximus IX Hero  Price: Rs. 23,350

Pros

Excellent feature set

Lots of connectivity including USB 3.1 Type-A and Type-C

High-end overclocking supported

Integrated Wi-Fi

Cons

Ratings (Out of 5)

Features: 4.5

Performance: 4.5

Value for Money: 4

Overall: 4.5

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