Scalable deep neural network accelerator cores with cubic integration using through chip interface

Abstract

Due to the recent advances in Deep Neural Network (DNN) technologies, recognition and inference applications are expected to run on mobile embedded systems. Developing high-performance and power-efficient DNN engines becomes one of the important challenges for embedded systems. Since DNN algorithms or structures are frequently updated, flexibility and performance scalability to deal with various types of networks are crucial requirement of the DNN accelerator design. In this paper, we describe the architecture and LSI design of a flexible and scalable CNN accelerator called SNACC (Scalable Neuro Accelerator Core with Cubic integration) which consists of several processing cores, on-chip memory modules, and ThruChip Interface (TCI).