A universal ANN-to-SNN framework for achieving high accuracy and low latency deep Spiking Neural Networks

S4-KD: A single step spiking SiamFC+ + for object tracking with knowledge distillation

Spiking neural networks (SNNs), which transmit information through binary spikes, have the advantages of high efficiency and low energy consumption. At present, the multiple time steps of SNNs can lead to increased latency and power consumption. To this end, we propose Single Step Spiking SiamFC+ + (S4), an improved single-step end-to-end direct training target tracking framework that compresses the time step to 1 by temporal pruning, using AlexNet as the backbone network. Experimental results show that, even when only a single time step is used, the tracking performance of the proposed S4 is still comparable to the original Spiking SiamFC+ +. Furthermore, we introduce the knowledge distillation to improve the performance of the proposed S4, which is called S4-KD for clarity. Three kinds of distillation loss functions are designed for the S4-KD. An artificial neural network model based on the AlexNet network serves as the teacher model, while the temporal-pruned S4 model acts as the student model for retraining. Experimental results show that the S4-KD tracker achieves higher performance on several tracking benchmarks. More specifically, on the OTB100 dataset, Precision and Success are 0.871 and 0.657 respectively, on the UAV123 dataset, Precision and Success are 0.766 and 0.603 respectively, and on the VOT2018 dataset, A, R, and EAO are 0.582, 0.370, and 0.278 respectively. In addition, the estimated energy consumption of the S4-KD is only 34.6 % of that of the original Spiking SiamFC+ +. To the best of our knowledge, the proposed S4-KD tracker surpasses all the existing SNN-based object tracking methods, achieving state-of-the-art performance. Our codes will be available at https://github.com/PSNN-xd/S4-KD.

Encrypted Spiking Neural Networks Based on Adaptive Differential Privacy Mechanism

Spike neural networks (SNNs) perform excellently in various domains. However, SNNs based on differential privacy (DP) protocols introduce uniform noise to the gradient parameters, which may affect the trade-off between model efficiency and personal privacy. Therefore, the adaptive differential private SNN (ADPSNN) is proposed in this work. It dynamically adjusts the privacy budget based on the correlations between the output spikes and labels. In addition, the noise is added to the gradient parameters according to the privacy budget. The ADPSNN is tested on four datasets with different spiking neurons including leaky integrated-and-firing (LIF) and integrate-and-fire (IF) models. Experimental results show that the LIF neuron model provides superior utility on the MNIST (accuracy 99.56%) and Fashion-MNIST (accuracy 92.26%) datasets, while the IF neuron model performs well on the CIFAR10 (accuracy 90.67%) and CIFAR100 (accuracy 66.10%) datasets. Compared to existing methods, the accuracy of ADPSNN is improved by 0.09% to 3.1%. The ADPSNN has many potential applications, such as image classification, health care, and intelligent driving.

Multi-compartment neuron and population encoding powered spiking neural network for deep distributional reinforcement learning

Inspired by the brain's information processing using binary spikes, spiking neural networks (SNNs) offer significant reductions in energy consumption and are more adept at incorporating multi-scale biological characteristics. In SNNs, spiking neurons serve as the fundamental information processing units. However, in most models, these neurons are typically simplified, focusing primarily on the leaky integrate-and-fire (LIF) point neuron model while neglecting the structural properties of biological neurons. This simplification hampers the computational and learning capabilities of SNNs. In this paper, we propose a brain-inspired deep distributional reinforcement learning algorithm based on SNNs, which integrates a bio-inspired multi-compartment neuron (MCN) model with a population coding approach. The proposed MCN model simulates the structure and function of apical dendritic, basal dendritic, and somatic compartments, achieving computational power comparable to that of biological neurons. Additionally, we introduce an implicit fractional embedding method based on population coding of spiking neurons. We evaluated our model on Atari games, and the experimental results demonstrate that it surpasses the vanilla FQF model, which utilizes traditional artificial neural networks (ANNs), as well as the Spiking-FQF models that are based on ANN-to-SNN conversion methods. Ablation studies further reveal that the proposed multi-compartment neuron model and the quantile fraction implicit population spike representation significantly enhance the performance of MCS-FQF while also reducing power consumption.