When it comes to novel computing architectures, whether in quantum, deep learning, or neuromorphic, it can be tricky to get a handle on how incremental improvements in processor counts translate to real world improvements since these bumps in element counts often don’t have perfect parallels to CPUs or even GPUs. …
Intel has moved a step closer to commercial neuromorphic computing with the development of a 64-chip system that provides eight million artificial neurons. …
A group of researchers at Sandia National Laboratories have developed a tool that can cross-train standard convolutional neural networks (CNN) to a spiking neural model that can be used on neuromorphic processors. …
We did not plan it, but today has become make-your-eyes-bleed-with-chip architecture-patent-applications day. …
Over the last several years we have seen many new hardware architectures emerge for deep learning training but this year, inference will have its turn in the spotlight. …
There are several competing processor efforts targeting deep learning training and inference but even for these specialized devices, the old performance ghosts found in other areas haunt machine learning as well. …
The novel architectures story is still shaping out for 2017 when it comes machine learning, hyperscale, supercomputing and other areas. …
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