Energy Efficient Approximate Arithmetic for Error Resilient Neuromorphic Computing

摘要

This brief proposes a novel design scheme for approximate adders and comparators to significantly reduce energy consumption while maintaining a very low error rate. The considerably improved error rate and critical path delay stem from the employed carry prediction technique that leverages the information from less significant input bits in a parallel manner. The proposed designs have been adopted in a VLSI-based neuromorphic character recognition chip with unsupervised learning implemented on chip. The approximation errors of the proposed arithmetic units have been shown to have negligible impact on the training process while archiving good energy efficiency.