A Configurable Approximate Multiplier for CNNs Using Partial Product Speculation

To improve the performance and energy efficiency of the compute-intensive convolutional neural networks (CNNs), approximate multipliers have widely been investigated, taking advantage of the inherent error tolerance in CNNs. However, as per their divergencies in the capability of error tolerance, different CNN models and datasets may require various accuracy in multiplication. Thus, in this paper, we propose an energy-efficient approximate multiplier with configurable accuracy to satisfy the continuously evolving requirements of CNNs. In this design, the approximation level is configured by changing the processing scheme for inputs due to their significance to accuracy. The correlations between partial products (PPs) are utilized to eliminate the generation and accumulation of some less significant PPs that are speculated by their adjacent more significant ones. Consequently, four approximate multiplier configurations are devised for 8x8 unsigned multiplication, denoted as AMPPS_S2, AMPPS_S3, AMPPS_S4, and AMPPS_S6. Compared with existing approximate multipliers, the proposed designs show significantly higher accuracy. Compared with an exact carry-save array multiplier, AMPPS_S2 can reduce the power dissipation, delay, and area by 21.7%, 35.1 %, and 24.4%, respectively. Moreover, to enhance the efficiency of the proposed approximate multiplier in systolic array-based hardware architectures for CNNs, a novel encoding strategy is proposed for storing the pre-trained weights. Obtaining a similar classification accuracy to the accurate implementation (tested in ResNet18 and ResNet50 on ImageNet), the 32x32 systolic array using AMPPS_S2 shows a 13.5% reduction in power consumption and a 19.2% reduction in area. Overall, the experimental results demonstrate that the proposed approximate multiplier results in higher accuracy in CNN-based image classification, with lower hardware overhead, compared with state-of-the-art approximate multipliers.