Computational Restructuring: Rethinking Image Compression Using Resistive Crossbar Arrays

Abstract

Image compression is performed on billions of edge devices deployed in the Internet of Things (IoT). The bottleneck of the compression is the 2-D discrete cosine transform (2D DCT), which involves performing two matrix-matrix multiplications in series. Earlier studies have explored directly mapping the 2D DCT computation to emerging resistive crossbar arrays (RCAs), which promise to perform matrix-vector multiplication (MVM) with extremely small energy-delay product. The main drawback is that the series computation is inherently vulnerable to errors. In this article, we propose to fundamentally rethink how to perform image compression using RCAs. The key idea is to restructure the computation to natively match the properties of the underlying resistive hardware. This allows three of the main design steps within image compression (2D DCT, quantization, and zig-zag reordering) to be integrated into a single analog MVM operation. The integration is facilitated by the development of a 2D DCT reconstruction technique, a frequency spectrum optimization technique, and a quantization optimization technique. The techniques improve the robustness to errors, eliminates the storage of intermediate data, enables processing of small image blocks, facilitates the utilization of large-scale RCAs, and reduces the requirements on the expensive domain interfaces. Compared with the previous work, the experimental results demonstrate significant improvements in image quality while reducing power and latency with up to 62% and 21%, respectively.