Computational Restructuring: Rethinking Image Processing using Memristor Crossbar Arrays
Baogang Zhanga, Necati Uysalb and Rickard Ewetzc
Department of Electrical and Computer Engineering, University of Central Florida, Orlando FL, USA
abaogang.zhang@knights.ucf.edu
bnecati@knights.ucf.edu
crickard.ewetz@ucf.edu
ABSTRACT
Image processing is a core operation performed on billions of sensor-devices in the Internet of Things (IoT). Emerging memristor crossbar arrays (MCAs) promise to perform matrix-vector multiplication (MVM) with extremely small energy-delay product, which is the dominating computation within the two-dimensional Discrete Cosine Transform (2D DCT). Earlier studies have directly mapped the digital implementation to MCA based hardware. The drawback is that the series computation is vulnerable to errors. Moreover, the implementation requires the use of large image block sizes, which is known to degrade the image quality. In this paper, we propose to restructure the 2D DCT into an equivalent single linear transformation (or MVM operation). The reconstruction eliminates the series computation and reduces the processed block sizes from NxN to √Nx √N. Consequently, both the robustness to errors and the image quality is improved. Moreover, the latency, power, and area is reduced with 2X while eliminating the storage of intermediate data, and the power and area can be further reduced with up to 62% and 74% using frequency spectrum optimization.
