# Classification Accuracy Improvement for Neuromorphic Computing Systems with One-level Precision Synapses Source: [Classification Accuracy Improvement for Neuromorphic Computing Systems with One-level Precision Synapses]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:31:19.733Z Authors: Yandan Wang, Wei Wen, Linghao Song, Hai Li Published: 2017-01-07T05:01:15.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()