Leveraging Residue Number System for Designing High-Precision Analog
  Deep Neural Network Accelerators
Cansu Demirkiran Rashmi Agrawal Vijay Janapa Reddi Darius Bunandar Ajay Joshi
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.