Bitcoin Mining Giant Bitmain Launches Deep Learning AI Hardware

“Bitmain saw trends in the AI business that were similar to the early days of Bitcoin."

Beijing-headquartered Bitmain, the world’s largest Bitcoin mining hardware producer, today announced its first products for accelerating artificial intelligence (AI) applications.

Gadi Glikberg

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Earlier this year Finance Magnates exclusively reported that Bitmain decided to enter the AI market after we visited the offices of Bitmaintech Israel – the company’s first R&D center outside of China. Today the head of Bitmaintech Israel, Gadi Glikberg, told us: “We are very proud to be part of the software development effort of the Sophon products. Our AI researchers are focused on creating computer vision and NLP solutions that are optimized for the Sophon platform.”

The new products are a customized tensor computing ASIC (Application Specific Integrated Circuit) optimized for training functions for deep learning networks and an advanced fan-cooled module that combines it into a compact, easy-to-integrate package. These can be applied in a variety of use cases including image and speech recognition, autonomous vehicle technology, enhanced security camera surveillance, robotics, the Internet of Things (IoT), and more.

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“Deep learning is very intensive computationally and our experience in creating high-performing hardware for Bitcoin has absolutely prepared us for this exciting area of computing,” notes Micree Zhan, Bitmain CEO. “AI hardware is an area that Bitmain is proactively developing to power the next generation of AI applications.

Bitmain saw trends in the AI business that were similar to the early days of Bitcoin, and so we started to explore AI toward the end of 2015. Now after only a year and a half, we have the mass-production chips in hand.”

The developers explain that the new products, called BM1680 and SC1, feature full compatibility with popular AI platforms, including mainstream Caffe, Darknet, Googlenet, VGG, Resnet, Yolo, Yoto2 and other models.

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