Classification Accuracy Improvement for Neuromorphic Computing Systems with One-level Precision Synapses
This work addresses accuracy degradation in neuromorphic systems for energy-efficient computing, representing an incremental improvement with specific gains.
The paper tackles the problem of limited synaptic weight resolution degrading accuracy in neuromorphic computing systems by proposing three orthogonal methods—distribution-aware quantization, quantization regularization, and bias tuning—to learn synapses with one-level precision, achieving accuracy drops within 0.19% for MNIST and 5.53% for CIFAR-10 compared to ideal systems.
Brain inspired neuromorphic computing has demonstrated remarkable advantages over traditional von Neumann architecture for its high energy efficiency and parallel data processing. However, the limited resolution of synaptic weights degrades system accuracy and thus impedes the use of neuromorphic systems. In this work, we propose three orthogonal methods to learn synapses with one-level precision, namely, distribution-aware quantization, quantization regularization and bias tuning, to make image classification accuracy comparable to the state-of-the-art. Experiments on both multi-layer perception and convolutional neural networks show that the accuracy drop can be well controlled within 0.19% (5.53%) for MNIST (CIFAR-10) database, compared to an ideal system without quantization.