In 1992, a Bell Labs researcher named Yann LeCun and several colleagues designed a chip that could run deep neural networks. We all know that later, they are more than twenty years ahead of the times.
LeCun now leads Facebook's AI Lab.
Deep neural network is not new, but it has not been considered as an important direction of AI development until recent years. Dedicated processing chips for deep neural networks are also one of the major research projects in this industry in recent years, whether Facebook, Google or Amazon.
Of course there are more players in this area. CNBC reported that several Google TPU developers left to start a similar company Groq. Intel bought a start-up company Nervana last year. IBM began in 2015. Qualcomm now starts working with Facebook.
On the other hand, running more efficient GPUs on deep neural networks is also threatening. Nvidia sets AI as one of the key development directions in the future. Last month they recruited Clément Farabet, a LeCun student at New York University, and later founded the deep learning company Madbits, which was acquired by Twitter in 2014.
Dedicated neural network special chips are equally important for the development of AR and VR: the recognition of objects and human faces has a demand for AI. However, if all the image data is directly connected to the cloud, then the impact on endurance will undoubtedly be very great. The solution is that the independent chip will process the data locally.
Google recently stated that TPU's savings in energy consumption are remarkable, and LeCun jokingly compared it: "If you don't want to blow up a small lake, it's best to replace it with dedicated hardware."
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