Tera-Scale Performance Machine Learning SoC (MLSoC) With Dual Stream Processor Architecture for Multimedia Content Analysis

Abstract

A new machine learning SoC (MLSoC) for multimedia content analysis is implemented with 16-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> area in 90-nm CMOS technology. Different from traditional VLSI architectures, it focuses on the coacceleration of computer vision and machine learning algorithms, and two stream processors with massively parallel processing elements are integrated to achieve tera-scale performance. In the dual stream processor (DSP) architecture, the data are transferred between processors and the high-bandwidth dual memory (HBDM) through the local media bus without consuming the AMBA AHB bandwidth. The image stream processor (ISP) of the MLSoC can handle common window-based operations for image processing, and the feature stream processor (FSP) can deal with machine learning algorithms with different dimensions. The power efficiency of the proposed MLSoC is 1.7 TOPS/W, and the area efficiency is 81.3 GOPS/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .