6 A 65nm 236.5nJ/Classification Neuromorphic Processor with 7.5% Energy Overhead On-Chip Learning Using Direct Spike-Only Feedback

Advances in neural network and machine learning algorithms have sparked a wide array of research in specialized hardware, ranging from high-performance convolutional neural network (CNN) accelerators to energy-efficient deep-neural network (DNN) edge computing systems 1. While most studies have focused on designing inference engines, recent works have shown that on-chip training could serve practical purposes such as compensating for process variations of in-memory computing 2 or adapting to changing environments in real time 3. However, these successes were limited to relatively simple tasks mainly due to the large energy overhead of the training process. These problems arise primarily from the high-precision arithmetic and memory required for error propagation and weight updates, in contrast to error-tolerant inference operation; the capacity requirements of a learning system are significantly higher than those of an inference system 4.