Novel 4:2 Approximate Compressor Designs for Multimedia and Neural Network Applications

Abstract

Low power dissipation in approximate arithmetic circuits has laid the foundation for area-efficient computational units for error resilient applications like image and signal processing. This paper proposes two novel low power high speed architectures for approximate 4:2 compressor that can be employed in multipliers for partial product summation. The two designs presented ([Formula: see text] and [Formula: see text]) have Error Distance (ED) of [Formula: see text] and Error Rate (ER) of 25%. The proposed [Formula: see text] and [Formula: see text] are able to achieve reduction in power and delay by (62.50%, 47.67%) and (83.13%, 60.20%), respectively, in comparison with the exact 4:2 compressor. To verify the effectiveness of the design, the proposed architectures are used to implement [Formula: see text] Dadda multiplier. The equal number of errors in positive and negative directions in the proposed designs aid in reducing the Mean Error Distance (MED) and Mean Relative Error Distance (MRED) of the multiplier. Multiplication of images and two-level decomposition of 2D Haar wavelets are implemented using the designed Dadda multiplier. The efficiency of the image processing applications is measured in terms of Mean Structural Similarity (MSSIM) index and Peak Signal-to-Noise Ratio (PSNR) and an average of 0.98 and 35[Formula: see text]dB, respectively, is obtained, which are in the acceptable range. In addition, a Convolutional Neural Network (CNN)-based LeNet-1 Handwritten Digit Recognition System (HDRS) is implemented using the proposed compressor-based multipliers. The proposed compressor-based architectures are able to achieve an average accuracy of 96.23%.