Matthew Griffin, described as “The Adviser behind the Advisers” and a “Young Kurzweil,” is the founder and CEO of the World Futures Forum and the 311 Institute, a global Futures and Deep Futures consultancy working between the dates of 2020 to 2070, and is an award winning futurist, and author of “Codex of the Future” series. Regularly featured in the global media, including AP, BBC, Bloomberg, CNBC, Discovery, RT, Viacom, and WIRED, Matthew’s ability to identify, track, and explain the impacts of hundreds of revolutionary emerging technologies on global culture, industry and society, is unparalleled. Recognised for the past six years as one of the world’s foremost futurists, innovation and strategy experts Matthew is an international speaker who helps governments, investors, multi-nationals and regulators around the world envision, build and lead an inclusive, sustainable future. A rare talent Matthew’s recent work includes mentoring Lunar XPrize teams, re-envisioning global education and training with the G20, and helping the world’s largest organisations envision and ideate the future of their products and services, industries, and countries. Matthew's clients include three Prime Ministers and several governments, including the G7, Accenture, Aon, Bain & Co, BCG, Credit Suisse, Dell EMC, Dentons, Deloitte, E&Y, GEMS, Huawei, JPMorgan Chase, KPMG, Lego, McKinsey, PWC, Qualcomm, SAP, Samsung, Sopra Steria, T-Mobile, and many more.
WHY THIS MATTERS IN BRIEF
This computer chip bucks the trend of producing smaller chips, and it’s been thrashing supercomputers.
In the world of computers chips are supposed to get smaller and more powerful – but one company, Cerebras Systems, has flipped that paradigm on its head and instead is creating incredibly powerful computer chips the size of dinner plates that give even supercomputers a run for their money. And now the world’s biggest AI chip just doubled its specs – without adding an inch.
The Cerebras Systems Wafer Scale Engine (WSE) is still about the size of a big dinner plate, and all that surface area enables a lot more of everything, from processors to memory. The first WSE chip, released in 2019, had an incredible 1.2 trillion transistors and 400,000 processing cores. Its successor doubles everything, except its physical size.
The WSE-2 crams in an amazing 2.6 trillion transistors and 850,000 cores on the same dinner plate. Its on-chip memory has increased from 18 gigabytes to 40 gigabytes, and the rate it shuttles information to and from said memory has gone from 9 petabytes per second to 20 petabytes per second – it’s a beast any way you slice it.
The WSE-2 is manufactured by Taiwan Semiconductor Manufacturing Company (TSMC), and it was a jump from TSMC’s 16 nanometer chipmaking process to its 7 nanometer process – skipping the 10 nanometer node – that enabled most of the WSE-2’s gains.
This required changes to the physical design of the chip, but Cerebras says they also made improvements to each core above and beyond what was needed to make the new process work. The updated mega-chip should be a lot faster and more efficient.
While Graphics Processing Units (GPUs) still reign supreme in Artificial Intelligence (AI) development, they weren’t made for AI in particular. Rather, GPUs were first developed and used for graphics heavy applications like gaming. They’ve done amazing things for AI and supercomputing, but in the last several years, specialised chips made for AI, for example from companies like Google and Facebook, are on the up and up.
The company likes to compare the WSE-2 to a top AI processor (NVIDIA’s A100) to underscore just how different it is from the competition. The A100 has two percent the number of transistors, at just 54.2 billion, occupying a little under two percent the surface area. It’s much smaller, but the A100’s might is more fully realised when hundreds or thousands of chips are linked together in a larger system.
Would you like chips with that? Courtesy: Cerberas
In contrast, the WSE-2 reduces the cost and complexity of linking all those chips together by jamming as much processing and memory as possible onto a single wafer of silicon. At the same time, removing the need to move data between lots of chips spread out on multiple server racks dramatically increases speed and efficiency.
The chip’s design gives its small, speedy cores their own dedicated memory and facilitates quick communication between cores. And Cerebras’s compiling software works with machine learning models using standard frameworks – like PyTorch and TensorFlow – to make distributing tasks among the chip’s cores fairly painless.
The approach is called wafer-scale computing because the chip is the size of a standard silicon wafer from which many chips are normally cut. Wafer-scale computing has been on the radar for years, but Cerebras is the first to make a commercially viable chip.
The chip comes packaged in a computer system called the CS-2. The system includes cooling and power supply and fits in about a third of a standard server rack.
After the startup announced the chip in 2019, it began working with a growing list of customers. Cerebras counts GlaxoSmithKline, Lawrence Livermore National Lab, and Argonne National, among others, as customers alongside unnamed partners in pharmaceuticals, biotech, manufacturing, and the military. Many applications have been in AI, but not all. Last year, the company said the National Energy Technology Laboratory (NETL) used the chip to outpace a supercomputer in a simulation of fluid dynamics. But, whether wafer-scale computing catches on remains to be seen.
Cerebras says their chip significantly speeds up machine learning tasks, and testimony from early customers – some of which claim pretty big gains – supports this. But there aren’t yet independent head-to-head comparisons. Neither Cerebras nor most other AI hardware startups, for example, took part in a recent MLperf benchmark test of AI systems where the top systems nearly all used Nvidia GPUs to accelerate their algorithms.
According to IEEE Spectrum, Cerebras says they’d rather let interested buyers test the system on their own specific neural networks as opposed to selling them on a more general and potentially less applicable benchmark. This isn’t an uncommon approach AI analyst Karl Freund said, “Everybody runs their own models that they developed for their own business. That’s the only thing that matters to buyers.”
It’s also worth noting the WSE can only handle tasks small enough to fit on its chip. The company says most suitable problems it’s encountered can fit, and the WSE-2 delivers even more space. Still, the size of machine learning algorithms is growing rapidly. Which is perhaps why Cerebras is keen to note that two or even three CS-2’s can fit into a server cabinet.
Ultimately, the WSE-2 doesn’t make sense for smaller tasks in which one or a few GPUs will do the trick. At the moment the chip is being used in large, compute-heavy projects in science and research. Current applications include cancer research and drug discovery, gravity wave detection, and fusion simulation. Cerebras CEO and cofounder Andrew Feldman says it may also be made available to customers with shorter-term, less intensive needs on the cloud.
The market for the chip is niche, but Feldman told HPC Wire it’s bigger than he anticipated in 2015, and it’s still growing as new approaches to AI are continually popping up.
“The market is moving unbelievably quickly,” he said.
The increasing competition between AI chips is worth watching. There may end up being several fit-to-purpose approaches or one that rises to the top, and fortunately for Cerebras for the moment, at least, it appears there’s some appetite for a generous helping of giant computer chips.