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 (BI), and its modified approximate version that we call approximate BI, as our baselines. The evaluation reveals that IABC brings about a block compression ratio of 25.7 on average (up to 106) against BI, with an average ratio of 2.69 (up to 45.0) and the Approximate BI 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.

中文翻译：

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一种用于容错图像数据的新型近似缓存块压缩器

在这项研究中，我们介绍了图像近似块压缩器（IABC），这是一种针对特定领域图像数据的快速（单周期）、简单且高性能的缓存块压缩器。我们的工作通过对选定的容错应用程序中使用的图像像素应用近似，提出了一种高质量的缓存块压缩技术。IABC 不仅可以与主流块压缩方法（包括零模式、频繁模式和部分模式检测）无缝配合，而且由于引入了近似，通过增加检测模式的概率来提高其性能。在检查了 IABC 的多个变体、所提出的具有单周期解压缩和两周期压缩延迟的块压缩之后，我们考虑了一种最先进的算法，即 Base-Delta-Immediate (BI) 及其修改后的近似值我们称之为近似 BI 的版本，作为我们的基线。评估结果显示，IABC 的块压缩比平均为 25.7（最高 106），而 BI 的平均比率为 2.69（最高 45.0），Approximate BI 的平均比率为 2.7（最高 45.2）。评估结果还表明，IABC 的压缩优势在深度学习对象识别应用程序的质量中平均误差仅为 2.73%。此外，IABC 还可以生成平均峰值信噪比 (PSNR) 为 39.49 dB 的高质量独立图像输出。上述品质仅需要 13% 的存储开销。