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takyon [soylentnews.org] writes:

http://www.nextplatform.com/2016/08/08/deep-learning-chip-upstart-set-take-gpus-task/ [nextplatform.com]

Bringing a new chip to market is no simple or cheap task, but as a new wave of specialized processors for targeted workloads brings fresh startup tales to bear, we are reminded again how risky such a business can be.

Of course, with high risk comes potential for great reward, that is, if a company is producing a chip that far outpaces general purpose processors for workloads that are high enough in number to validate the cost of design and production. The stand-by figure there is usually stated at around $50 million, but that is assuming a chip requires validation, testing, and configuration rounds to prove its ready to be plugged into a diverse array of systems. Of course, if one chooses to create and manufacture a chip and make it available only via a cloud offering or as an appliance, the economics change—shaving off more than a few million.

These sentiments are echoed by Naveen Rao, CEO of Nervana Systems [nervanasys.com], a deep learning startup that has put its $28 million in funding to the TSMC 28 nanometer test with a chip expected in Q1 of 2017. With a cloud-based deep learning business to keep customers, including Monsanto, on the hook for deep learning workloads crunched via their on-site, TitanX GPU cluster stacked with their own "Neon" software libraries for accelerated deep learning training and inference, the company has been focused on the potential for dramatic speedups via their stripped-down tensor-based architecture in the forthcoming Nervana Engine processor.

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