Towards Lossless ANN-SNN Conversion under Ultra-Low Latency with Dual-Phase Optimization

Spiking neural network (SNN) operating with asynchronous discrete events shows higher energy efficiency. A popular approach to implement deep SNNs is ANN-SNN conversion combining both efficient training in ANNs and efficient inference in SNNs. However, the previous works mostly required thousands of time steps to achieve lossless conversion. In this paper, we first identify the underlying cause, i.e., misrepresentation of the negative or overflow residual membrane potential in SNNs. Furthermore, we systematically analyze the conversion error between SNNs and ANNs, and then decompose it into three folds: quantization error, clipping error, and residual membrane potential representation error. With such insights, we propose a dual-phase conversion algorithm to minimize those errors. As a result, our model achieves SOTA in both accuracy and accuracy-delay tradeoff with deep architectures (ResNet and VGG net). Specifically, we report SOTA accuracy within 16$\times$ speedup compared with the latest results. Meanwhile, lossless conversion is performed with at least 2$\times$ faster reasoning performance.

Integer-Only Neural Network Quantization Scheme Based on Shift-Batch-Normalization

Neural networks are very popular in many areas, but great computing complexity makes it hard to run neural networks on devices with limited resources. To address this problem, quantization methods are used to reduce model size and computation cost, making it possible to use neural networks on embedded platforms or mobile devices. In this paper, an integer-only-quantization scheme is introduced. This scheme uses one layer that combines shift-based batch normalization and uniform quantization to implement 4-bit integer-only inference. Without big integer multiplication(which is used in previous integer-only-quantization methods), this scheme can achieve good power and latency efficiency, and is especially suitable to be deployed on co-designed hardware platforms. Tests have proved that this scheme works very well for easy tasks. And for tough tasks, performance loss can be tolerated for its inference efficiency. Our work is available on github: https://github.com/hguq/IntegerNet.