A 65-nm Neuromorphic Image Classification Processor With Energy-Efficient Training Through Direct Spike-Only Feedback

Recent advances in neural network (NN) and machine learning algorithms have sparked a wide array of research in specialized hardware, ranging from high-performance NN accelerators for use inside the server systems to energy-efficient edge computing systems. While most of these studies have focused on designing inference engines, implementing the training process of an NN for energy-constrained mobile devices has remained to be a challenge due to the requirement of higher numerical precision. In this article, we aim to build an on-chip learning system that would show highly energy-efficient training for NNs without degradation in the performance for machine learning tasks. To achieve this goal, we adapt and optimize a neuromorphic learning algorithm and propose hardware design techniques to fully exploit the properties of the modifications. We verify that our system achieves energy-efficient training with only 7.5% more energy consumption compared with its highly efficient inference of 236 nJ/image on the handwritten digit [Modified National Institute of Standards and Technology database (MNIST)] images. Moreover, our system achieves 97.83% classification accuracy on the MNIST test data set, which outperforms prior neuromorphic on-chip learning systems and is close to the performance of the conventional method for training deep neural networks (NNs), the backpropagation.