Spiking-HDC: A Spiking Neural Network Processor with HDC Classifier Enabling Transfer Learning

This work proposes Spiking-HDC, a spiking neural network (SNN) processing system with hyperdimensional computing (HDC) and its hardware design for domain transfer scenarios. The input data is firstly fed into a two-layer SNN, serving as a feature extractor. It is followed by a HDC classifier to process feature vectors using hypervectors in binary representation. Such a system leverages HDC’s capability of single-pass learning, which can be adopted to rapidly updating but highly similar tasks by fine-tuning the HDC classifier with limited labeled data. By our experiments, the proposed system demonstrates transfer learning accuracy of 94.76%, 87.12% and 94.37% with few-shot samples on N-MNIST, DVS-Gesture and MNIST datasets, respectively. To apply Spiking-HDC model to extreme edge inference tasks, a dedicated processor is designed and implemented. The simulated results in 40 nm CMOS process illustrate that it has 0.88 mm ² core area and 1.8 mW power at 100 MHz frequency. In comparison to similar works, it achieves 3.8×-36× inference energy efficiency enhancement.