A Near-Threshold Spiking Neural Network Accelerator With a Body-Swapping-Based In Situ Error Detection and Correction Technique

Specialized architecture combined with near- and subthreshold voltage circuits emerges as a promising candidate to improve the energy efficiency in performing complex computing kernels in a resource-constrained device. One of the critical challenges in such design is the large delay variability across process, voltage, and temperature (PVT) variations. The in situ error detection and correction (EDAC) technique can potentially handle such variations; however, since existing techniques have targeted von-Neumann architecture and nominal voltage circuits, it becomes nontrivial to apply them on near and subthreshold voltage accelerators, many of which do not base on the von-Neumann architecture. In particular, those accelerators often have no instruction, making it difficult to use the popular instruction-replay-based error correction. To tackle this challenge, in this paper, we propose a novel in situ EDAC technique that utilizes dynamic, temporarily, and spatially fine-grained body swapping for error correction without instruction replay. Using the proposed technique, we prototyped a spiking neural network (SNN) sorter in non-von-Neumann architecture. The prototyped chip can successfully remove the worst-case margin and, thus, achieve 49.3% higher energy efficiency and 35.6% higher throughput compared to the baseline that operates with the worst-case margin. The proposed technique incurs only 4.1% silicon area overhead and requires no additional supply voltage.