Abstract
Conference Title: 2018 New Generation of CAS (NGCAS) Conference Start Date: 2018, Nov. 20 Conference End Date: 2018, Nov. 23 Conference Location: Valletta, Malta Power consumption is a crucial design aspect in multimedia and machine learning applications. Approximate computing offers an energy-efficient approach for both power reduction and area optimization. In this paper, a hybrid approximation methodology based on error tolerant multipliers (ETMs) is introduced. The proposed design splits the approximation process into two parts: (1) approximating the most significant bits (MSBs) using approximate logarithms and (2) approximating the least significant bits (LSBs) using truncation. A prototype of the proposed multiplier is demonstrated with an image processing application (JPEG compression) using a Discrete Cosine Transform (DCT) where the power delay product (PDP) is improved by 1.9X. And the area utilization is reduced by 2.7X with only 20% reduction in the output image peak signal-to-noise ratio (PSNR).