Spiking Neural Encoding Schemes and STDP Training Algorithms for Edge Computing

To enhance real-time data processing, edge computing is utilized in a wider and wider range of applications. For the areas that require large bandwidth and low latency, edge computing even becomes a must. For instance, in the communication area, spectrum sharing within multiple users requires high accuracy of spectrum using prediction as well as low latency. For such tasks, neuromorphic computing, especially spiking neural networks (SNNs), can be a potential method because of its power and silicon area efficiency. In this paper, we have discussed various kinds of spiking neural encoding schemes and their integrated circuit (IC) implementations. We have also summarized the pair-based STDP and the triplet-based STDP learning rule, their mathematical models, and the triplet-based reconfigurable circuit implementation. The Pytorch simulation of different encoding schemes working with two STDP rules for the MNIST and a dynamic spectrum sensing dataset is also presented. It shows that multiplexing ISI-phase encoder can achieve at most 8.9% higher accuracy than other encoders, and TSTDP provides 2.7% higher accuracy than PSTDP for the MNIST dataset. What's more, for the task of spectrum sensing for edge computing, the multiplexing encoding is also 4.3% more accurate, and TSTDP is 0.3% more accurate for the spectrum utilization prediction.