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Analog computing has reemerged as a promising avenue for accelerating deepneural networks (DNNs) due to its potential to overcome the energy efficiencyand scalability challenges posed by traditional digital architectures. However,achieving high precision and DNN accuracy using such technologies ischallenging, as high-precision data converters are costly and impractical. Inthis paper, we address this challenge by using the residue number system (RNS).RNS allows composing high-precision operations from multiple low-precisionoperations, thereby eliminating the information loss caused by the limitedprecision of the data converters. Our study demonstrates that analogaccelerators utilizing the RNS-based approach can achieve ${\geq}99\%$ of FP32accuracy for state-of-the-art DNN inference using data converters with only$6$-bit precision whereas a conventional analog core requires more than $8$-bitprecision to achieve the same accuracy in the same DNNs. The reduced precisionrequirements imply that using RNS can reduce the energy consumption of analogaccelerators by several orders of magnitude while maintaining the samethroughput and precision. Our study extends this approach to DNN training,where we can efficiently train DNNs using $7$-bit integer arithmetic whileachieving accuracy comparable to FP32 precision. Lastly, we present afault-tolerant dataflow using redundant RNS error-correcting codes to protectthe computation against noise and errors inherent within an analog accelerator.

A Blueprint for Precise and Fault-Tolerant Analog Neural Networks