The answer to the question of how to make the best of AI hardware may not be solely, or even primarily, related to hardware
How do you make the best out of the proliferating array of emerging custom silicon hardware while not spreading yourself thin to keep up with each and every one of them?
If we were to put a price tag on that question, it would be in the multi-billion dollar territory. That’s what the combined estimated value of the different markets it touches upon is. As AI applications are exploding, so is the specialized hardware that supports them. Read More
Tag Archives: Nvidia
Artificial Intelligence is a Supercomputing problem
The next generation of Artificial Intelligence applications impose new and demanding computing infrastructures. How are the computer systems that support artificial intelligence? How did we get here? Who has access to these systems? What is our responsibility as Artificial Intelligence practitioners?
[These posts will be used in the master course Supercomputers Architecture at UPC Barcelona Tech with the support of the BSC]
Part 1
Part 2
Von Neumann Is Struggling
In an era dominated by machine learning, the von Neumann architecture is struggling to stay relevant.
The world has changed from being control-centric to one that is data-centric, pushing processor architectures to evolve. Venture money is flooding into domain-specific architectures (DSA), but traditional processors also are evolving. For many markets, they continue to provide an effective solution.
The von Neumann architecture for general-purpose computing was first described in 1945 and stood the test of time until the turn of the Millennium. … Scalability slowed around 2000. Then, Dennard scaling reared its head in 2007 and power consumption became a limiter. While the industry didn’t recognize it at the time, that was the biggest inflection point in the industry to date. It was the end of instruction-level parallelism. Read More
Machine learning at the speed of light: New paper demonstrates use of photonic structures for AI
As we enter the next chapter of the digital age, data traffic continues to grow exponentially. To further enhance artificial intelligence and machine learning, computers will need the ability to process vast amounts of data as quickly and as efficiently as possible.
Conventional computing methods are not up to the task, but in looking for a solution, researchers have seen the light—literally.
Light-based processors, called photonic processors, enable computers to complete complex calculations at incredible speeds. New research published this week in the journal Nature examines the potential of photonic processors for artificial intelligence applications. The results demonstrate for the first time that these devices can process information rapidly and in parallel, something that today’s electronic chips cannot do. Read More
Accelerating AI computing to the speed of light
Artificial intelligence and machine learning are already an integral part of our everyday lives online. … As the demands for AI online continue to grow, so does the need to speed up AI performance and find ways to reduce its energy consumption. Now a team of researchers has come up with a system that could help: an optical computing core prototype that uses phase-change material. This system is fast, energy efficient and capable of accelerating the neural networks used in AI and machine learning. The technology is also scalable and directly applicable to cloud computing. The team published these findings Jan. 4 in Nature Communications. Read More
Nvidia developed a radically different way to compress video calls
Nvidia Maxine uses Generative Adversarial Networks to re-create video frames.
Last month, Nvidia announced a new platform called Maxine that uses AI to enhance the performance and functionality of video conferencing software. The software uses a neural network to create a compact representation of a person’s face. This compact representation can then be sent across the network, where a second neural network reconstructs the original image—possibly with helpful modifications.
Nvidia says that its technique can reduce the bandwidth needs of video conferencing software by a factor of 10 compared to conventional compression techniques. It can also change how a person’s face is displayed. For example, if someone appears to be facing off-center due to the position of her camera, the software can rotate her face to look straight instead. Software can also replace someone’s real face with an animated avatar. Read More
Steve Jobs’s last gambit: Apple’s M1 Chip
Even as Apple’s final event of 2020 gradually becomes a speck in the rearview mirror, I can’t help continually thinking about the new M1 chip that debuted there. I am, at heart, an optimist when it comes to technology and its impact on society. And my excitement about the new Apple Silicon is not tied to a single chip, a single computer, or a single company. It is really about the continuing — and even accelerating — shift to the next phase of computing.
… Today, we need our computers to be capable of handling many tasks — and doing so with haste. The emphasis is less on performance and more about capabilities. Everyone is heading toward this future, including Intel, AMD, Samsung, Qualcomm, and Huawei. But Apple’s move has been more deliberate, more encompassing, and more daring.
Steve Jobs’s last gambit was challenging the classic notion of the computer, and the M1 is Apple’s latest maneuver. Read More
FPGAs could replace GPUs in many deep learning applications
The renewed interest in artificial intelligence in the past decade has been a boon for the graphics cards industry. Companies like Nvidia and AMD have seen a huge boost to their stock prices as their GPUs have proven to be very efficient for training and running deep learning models. Nvidia, in fact, has even pivoted from a pure GPU and gaming company to a provider of cloud GPU services and a competent AI research lab.
But GPUs also have inherent flaws that pose challenges in putting them to use in AI applications, according to Ludovic Larzul, CEO and co-founder of Mipsology, a company that specializes in machine learning software.
The solution, Larzul says, are field programmable gate arrays (FPGA), an area where his company specializes. Read More
Inventing Virtual Meetings of Tomorrow with NVIDIA AI Research
NVIDIA Maxine is a fully accelerated platform SDK for developers of video conferencing services to build and deploy AI-powered features that use state-of-the-art models in their cloud. Video conferencing applications based on Maxine can reduce video bandwidth usage down to one-tenth of H.264 using AI video compression, dramatically reducing costs. Read More
#nvidia, #videos, #image-recognitionAlgorithms are not enough
The next breakthrough in AI requires a rethinking of our hardware
Today’s AI has a problem: it is expensive. Training Resnet-152, a modern computer vision model, is estimated to cost around 10 Billion floating point operations, which is dwarfed by modern language models. Training GPT-3, the recent natural language model from OpenAI, is estimated to cost 300 Billion Trillion floating point operations, which costs at least $5M on commercial GPUs. Compare this to the human brain, which can recognize faces, answer questions, and drive cars with as little as a banana and a cup of coffee. Read More
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