Energy-Efficient Neural Computing with Approximate Multipliers

• Published in: ACM journal on emerging technologies in computing systems 2018-07, Vol.14 (2), p.1-23
• Main Authors: Sarwar, Syed Shakib, Venkataramani, Swagath, Ankit, Aayush, Raghunathan, Anand, Roy, Kaushik
• Format: Article
• Language: English
• ISSN: 1550-4832; 1550-4840
• DOI: 10.1145/3097264

Neural networks, with their remarkable ability to derive meaning from a large volume of complicated or imprecise data, can be used to extract patterns and detect trends that are too complex for the von Neumann computing paradigm. Their considerable computational requirements stretch the capabilities of even modern computing platforms. We propose an approximate multiplier that exploits the inherent application resilience to error and utilizes the notion of computation sharing to achieve improved energy consumption for neural networks. We also propose a Multiplier-less Artificial Neuron (MAN), which is even more compact and energy efficient. We also propose a network retraining methodology to recover some of the accuracy loss due to the use of these approximate multipliers. We evaluated the proposed algorithm/design on several recognition applications. The results show that we achieve ∼33%, ∼32%, and ∼25% reduction in power consumption and ∼33%, ∼34%, and ∼27% reduction in area, respectively, for 12-, 8-, and 4-bit MAN, with a maximum ∼2.4% loss in accuracy compared to a conventional neuron implementation of equivalent bit precision. These comparisons were performed under iso-speed conditions.