Google's deep learning finds a critical path in AI chips

Characteristic to many AI chips are parallel, identical processor elements for masses of simple math operations, here called a "PE," for doing lots of vector-matrix multiplications that are the workhorse of neural net processing. A year ago, ZDNet spoke with Google Brain director Jeff Dean about how the company is using artificial intelligence to advance its internal development of custom chips to accelerate its software. Dean noted that deep learning forms of artificial intelligence can in some cases make better decisions than humans about how to lay out circuitry in a chip. This month, Google unveiled to the world one of those research projects, called Apollo, in a paper posted on the arXiv file server, "Apollo: Transferable Architecture Exploration," and a companion blog post by lead author Amir Yazdanbakhsh. Apollo represents an intriguing development that moves past what Dean hinted at in his formal address a year ago at the International Solid State Circuits Conference, and in his remarks to ZDNet.