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. …
Ziyang Xu from Peking University in Beijing sees several similarities between the human brain and Von Neumann computing devices. …
The novel architectures story is still shaping out for 2017 when it comes machine learning, hyperscale, supercomputing and other areas. …
There are plenty of things that the members of the high performance community do not agree on, there is a growing consensus that machine learning applications will at least in some way be part of the workflow at HPC centers that do traditional simulation and modeling. …
It is difficult to shed a tear for Moore’s Law when there are so many interesting architectural distractions on the systems horizon. …
If the name Kwabena Boahen sounds familiar, you might remember silicon that emerged in the late 1990s that emulated the human retina. …
Much of the talk around artificial intelligence these days focuses on software efforts – various algorithms and neural networks – and such hardware devices as custom ASICs for those neural networks and chips like GPUs and FPGAs that can help the development of reprogrammable systems. …
Like all hardware device makers eager to meet the newest market opportunity, Intel is placing multiple bets on the future of machine learning hardware. …
Just a few years ago, the promise of ultra-low power, high performance computing was tied to the rather futuristic-sounding vision of a “brain chip” or neuromorphic processor, which could mimic the brain’s structure and processing ability in silicon—quickly learning and chewing on data as fast as it could be generated. …
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