While securing the high-end particle physics market segment is not likely to push any of the AI/ML ASICS into competition with GPUs anytime soon, the chipmakers that can prove their value on some of the most demanding, real-time AI workloads can capture some serious mindshare. …
Every chipmaker has an emerging silicon photonics story but as happens only rarely, it could be a small startup that will push them to productize faster. …
The concepts underpinning vector databases are decades old, but it is only relatively recently that these are the underlying “secret weapon” of the largest webscale companies that provide services like search and near real-time recommendations. …
SPONSORED SambaNova Systems is a technology startup founded in 2017 by a group of far-sighted engineers and data scientists who saw that the current approaches to AI and machine learning were beginning to run out of steam, and that an entire new architecture would be necessary in order to make AI accessible for everyone as well as deliver the scale, performance, accuracy and ease of use needed for future applications. …
There is a new challenge workload on the horizon, one where few can afford to compete. …
The battle for HPC centers and national labs is underway among the leading AI chip startups in the high-end datacenter space (Graphcore, Cerebras, and SambaNova in particular). …
Investigations, conducted together with scientists at CERN, show promising results – with breakthrough performance – in their pursuit of faster Monte Carlo based simulations, which are an important part of many scientific, engineering, and financial applications. …
…With performance comparable to the Nvidia V100 GPU, a common accelerator in HPC but better energy consumption numbers and memory bandwidth potential, Graphcore can turn heads in supercomputing.
For those who are at the intersection of AI hardware and software, the open source Apache TVM effort is already well known and used among a number of chipmakers as well as developers. …
There could be a new era of codesign dawning for machine learning, one that moves away from the training and inference separations and toward far less dense networks with highly sparse weights and activations. …
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