A novel approximate cache block compressor for error-resilient image data

Abstract

In this research, we introduce the Image Approximate Block Compressor (IABC), a fast (single cycle), simple and high-performance cache block compressor targeting domain-specific image data. Our work presents a high-quality cache block compression technique by applying approximation to image pixels used in selected error-resilient applications. IABC not only works seamlessly alongside mainstream block compression approaches including zero, frequent and partial patterns detection but also, due to introducing the approximation, improves their performance by increasing the probability of detecting the patterns. Having examined multiple variants of IABC, the proposed block compression with one-cycle decompression and two-cycle compression latency, we have considered a state-of-the-art algorithm, namely Base-Delta-Immediate (BΔI), and its modified approximate version that we call approximate BΔI, as our baselines. The evaluation reveals that IABC brings about a block compression ratio of 25.7 on average (up to 106) against BΔI, with an average ratio of 2.69 (up to 45.0) and the Approximate BΔI with an average ratio of 2.7 (up to 45.2). The evaluation results also show that the compression benefits of IABC come at only a 2.73% average error in the quality of a deep learning object recognition application. In addition, IABC generates high-quality outputs for stand-alone images with a 39.49 dB average Peak Signal to Noise Ratio (PSNR). The mentioned qualities come at only 13% storage overhead.