  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.
