Toshiba Presents Low Power Deep Learning Processor

Optics.org: Toshiba develops a Time Domain Neural Network (TDNN), said to have an extremely low power consumption. TDNN is composed of a massive number of tiny processing units that use Toshiba’s original analog technique, unlike conventional digital processors. TDNN was reported on November 8 at A-SSCC 2016 (Asian Solid-State Circuits Conference 2016).

In von Neumann type computer, most energy is consumed moving data from on-chip or off-chip memory devices to the processing unit. The most effective way to reduce movement of a datum is to have massive numbers of processing units, each dedicated to handling only one datum that is located close by. These datum points are given a weight during conversion of an input signal (e.g. an image of a cat) to an output signal (e.g. the recognition of the image as a cat). The closer the datum point is to the desired output, the higher the weight it is given. The weight provides a parameter that automatically guides the deep learning process.

The reported energy consumption per operation is 20.6 fJ, which is 1/6x better than previously reported at ISSCC 2016.