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Achieving high accuracy, while maintaining good energy efficiency, in analogDNN accelerators is challenging as high-precision data converters areexpensive. In this paper, we overcome this challenge by using the residuenumber system (RNS) to compose high-precision operations from multiplelow-precision operations. This enables us to eliminate the information losscaused by the limited precision of the ADCs. Our study shows that RNS canachieve 99% FP32 accuracy for state-of-the-art DNN inference using dataconverters with only $6$-bit precision. We propose using redundant RNS toachieve a fault-tolerant analog accelerator. In addition, we show that RNS canreduce the energy consumption of the data converters within an analogaccelerator by several orders of magnitude compared to a regular fixed-pointapproach.

Leveraging Residue Number System for Designing High-Precision Analog Deep Neural Network Accelerators