Origami: A 803 GOp/s/W Convolutional Network Accelerator

TL;DR

This paper introduces a highly efficient convolutional neural network accelerator that achieves 803 GOp/s/W, enabling scalable, high-performance deep learning processing for embedded systems.

Contribution

The paper presents the first silicon implementation of a CNN accelerator with unprecedented power efficiency and scalability to TOp/s performance.

Findings

Achieves 196 GOp/s on 3.09 mm^2 silicon

Power efficiency of 803 GOp/s/W

Scalable architecture for TOp/s performance

Abstract

An ever increasing number of computer vision and image/video processing challenges are being approached using deep convolutional neural networks, obtaining state-of-the-art results in object recognition and detection, semantic segmentation, action recognition, optical flow and superresolution. Hardware acceleration of these algorithms is essential to adopt these improvements in embedded and mobile computer vision systems. We present a new architecture, design and implementation as well as the first reported silicon measurements of such an accelerator, outperforming previous work in terms of power-, area- and I/O-efficiency. The manufactured device provides up to 196 GOp/s on 3.09 mm^2 of silicon in UMC 65nm technology and can achieve a power efficiency of 803 GOp/s/W. The massively reduced bandwidth requirements make it the first architecture scalable to TOp/s performance.