There has been a great deal of interest in deep learning chip startup, Graphcore, since we first got the limited technical details of the company’s first-generation chip last year, which was followed by revelations about how their custom software stack can run a range of convolutional, recurrent, generative adversarial neural network jobs. …
The last two years have delivered a new wave of deep learning architectures designed specifically for tackling both training and inference sides of neural networks. …
As a thought exercise, let’s consider neural networks as massive graphs and begin considering the CPU as a passive slave to some higher order processor—one that can sling itself across multiple points on an ever-expanding network of connections feeding into itself, training, inferencing, and splitting off into multiple models on the same architecture. …
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If we could sum up the near-term future of high performance computing in a single phrase, it would be more of the same and then some. …
It is funny to think that in a certain light, AMD has Big Blue to thank for its resurgence in the datacenter. …
Microsoft Azure has been able to put actual Cray XC series supercomputers and CS Storm clusters in the public cloud for more than two years now, and it is unclear how many companies have commissioned Cray, now part of Hewlett Packard Enterprise, to do so. …
It’s par for the course for AI chip startups to focus on peak performance on outdated benchmarks to appeal to the hardware folks who might give their gear a go for deep learning training or inference. …
Startup Cerebras Systems has unveiled the world’s largest microprocessor, a waferscale chip custom-built for machine learning. …
We have to admit that it is often a lot more fun watching an upstart carve out whole new slices of business, or create them out of what appears to be thin air, in the datacenter than it is to watch how it will respond to intense competitive pressures and somehow manage to keep growing despite that. …
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