Boosting the Clock for High Performance FPGA Inference
A few years ago the market was rife with deep learning chip startups aiming at AI training. …
A few years ago the market was rife with deep learning chip startups aiming at AI training. …
We did not plan it, but today has become make-your-eyes-bleed-with-chip architecture-patent-applications day. …
While deep learning models might not be able to simulate large-scale physical phenomena in the same way purpose-built supercomputers and their application stacks do, there is more research emerging that shows how traditional HPC simulations can be augmented, if not replaced in some parts, by neural networks. …
For any public cloud to succeed, it has to offer best of breed technologies reasonably close to the cutting edge and supporting the wide variety of compute that the enterprises of the world would otherwise acquire and run on premises. …
If it isn’t obvious, we like hardware here at The Next Platform. …
By definition, HPC is always at the cutting edge of computing, driving innovations in processor, system, and software design that eventually find their way into more mainstream computing systems. …
This week we have heard much about the inference side of the deep learning workload, with a range of startups emerging at the AI Hardware Summit. …
On the hardware side, the next frontier for deep learning innovation will be in getting the performance, efficiency, and accuracy needed for inference at scale. …
Gentlemen (and women), start your inference engines.
One of the world’s largest buyers of systems is entering evaluation mode for deep learning accelerators to speed services based on trained models. …
When it comes to machine learning, a lot of the attention in the past six years has focused on the training of neural networks and how the GPU accelerator radically improved the accuracy of networks, thanks to its large memory bandwidth and parallel compute capacity relative to CPUs. …
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