There are many established and startup companies developing deep learning chips.

Google and Wave Computing have working silicon and are conducting customer trials.

* Wave Computing says its 3U deep learning server can train AlexNet in 40 minutes, three times faster than NVIDIA’s P100 DGX-1 server.
* Wave Computing’s claim that its TPU is 1000 times faster.
* NVIDIA has improved the architectural efficiency of its GPUs by roughly 10x over the last few years

Chinese AI chip startup has received $100 million in funding.

Cambricon Technologies aims to have one billion smart devices using its AI processor and own 30% of China’s high-performance AI chip market in three years.

Huawei estimates Cambricon chips are six times faster for deep-learning applications like training algorithms to identify images than a GPU.

The Cambricon-1H8 focuses on lower power consumption visual application, providing up to 2.3 times the performance per watt over its predecessor Cambricon-1A chip. The Cambricon-1H16, has wider application and better performance. While the Cambricon-1M is made for intelligent driving and has 10 times the performance of Cambricon-1A.

 

 

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