31 Oct '17, 8pm

Architectures Battle for Deep Learning | EE Times

Chip vendors implement new applications in CPUs. If the application is suitable for GPUs and DSPs, it may move to them next. Over time, companies develop ASICs and ASSPs. Is Deep learning is moving through the same sequence? In the brief history of deep neural networks (DNNs), users have tried several hardware archi­tec­tures to increase their performance. General-purpose CPUs are the easiest to program but are the least efficient in performance per watt. GPUs are optimized for parallel floating-point computa­tion and provide several times better performance than CPUs. As GPU vendors discovered a siza­ble new customer base, they began to enhance their designs to further improve DNN throughput. For example, Nvidia’s new Volta architecture adds dedicated matrix-multiply units, accelerating a common DNN operation. Even these enhanced GPUs remain burdened by their graphics-spe...

Full article: https://www.eetimes.com/author.asp?section_id=36&doc_id=1...

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