  Achieving high accuracy, while maintaining good energy efficiency, in analog
DNN accelerators is challenging as high-precision data converters are
expensive. In this paper, we overcome this challenge by using the residue
number system (RNS) to compose high-precision operations from multiple
low-precision operations. This enables us to eliminate the information loss
caused by the limited precision of the ADCs. Our study shows that RNS can
achieve 99% FP32 accuracy for state-of-the-art DNN inference using data
converters with only $6$-bit precision. We propose using redundant RNS to
achieve a fault-tolerant analog accelerator. In addition, we show that RNS can
reduce the energy consumption of the data converters within an analog
accelerator by several orders of magnitude compared to a regular fixed-point
approach.
