Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects

TL;DR

This work introduces the Segregated Temporal Probe (STP), an analytical tool to rigorously evaluate the temporal processing capabilities of Spiking Neural Networks (SNNs) and to diagnose deficiencies in existing neuromorphic benchmarks. It establishes a three-task temporal benchmark suite (PTB, PS-MNIST, Binary Adding) and conducts a comprehensive study of over thirty SNN approaches, uncovering improvements from new neuron models and architectures but a persistent gap to non-spiking models on long-range dependencies. The findings show that many current benchmarks inadequately probe temporal processing, while optimized learning rules, surrogate gradients, normalization, and delay-aware architectures can significantly boost temporal performance. The paper offers an open-source benchmarking library and argues for dedicated neuromorphic benchmarks and training strategies tailored to real-world temporal tasks, highlighting practical implications for energy-efficient temporal processing systems.

Abstract

Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking Neural Networks (SNNs) excel in handling such data with high efficiency, owing to their rich neuronal dynamics and sparse activity patterns. Given the recent surge in the development of SNNs, there is an urgent need for a comprehensive evaluation of their temporal processing capabilities. In this paper, we first conduct an in-depth assessment of commonly used neuromorphic benchmarks, revealing critical limitations in their ability to evaluate the temporal processing capabilities of SNNs. To bridge this gap, we further introduce a benchmark suite consisting of three temporal processing tasks characterized by rich temporal dynamics across multiple timescales. Utilizing this benchmark suite, we perform a thorough evaluation of recently introduced SNN approaches to elucidate the current status of SNNs in temporal processing. Our findings indicate significant advancements in recently developed spiking neuron models and neural architectures regarding their temporal processing capabilities, while also highlighting a performance gap in handling long-range dependencies when compared to state-of-the-art non-spiking models. Finally, we discuss the key challenges and outline potential avenues for future research.

Figures (11)

• Figure 1: (a) Overview of the paper organization. In Section \ref{['sec:STP']}, we propose the Segregated Temporal Probe (STP) analytical tool for assessing the effectiveness of neuromorphic benchmarks in evaluating the temporal processing capabilities of SNN. In Section \ref{['sec:eval_bench']}, using the STP, we discover that commonly used neuromorphic benchmarks are ineffective for assessing temporal processing performance. Then, we introduce a suite of temporal processing benchmarks in Section \ref{['sec:benchmark']}. Based on this suite, we conduct a comprehensive benchmarking study to reveal the current status of SNNs for temporal processing in Section \ref{['sec:benchmarking']}. Finally, we discuss key challenges and outline future directions in Section \ref{['sec:challenges']}. (b) Illustration of the proposed STP. STP incorporates three algorithms (STBP, SDBP, and NoTD) that systematically disrupts the temporal processing pathways within an SNN to elucidate their significance.
• Figure 2: Visualization of samples in neuromorphic benchmarks. (a) Samples from event-based vision datasets: N-MNIST, CIFAR10-DVS, and DvsGesture (from top to bottom). (b) Samples from neuromorphic audio datasets: SHD (top) and SSC (bottom).
• Figure 3: Comparison of the computational graphs for three algorithms utilized in the STP. Forward and backward passes are denoted by black and red arrows, respectively.
• Figure 4: Qualitative results of samples from the DvsGesture dataset, along with the confident frame (highlighted with red boxes) selected by all the algorithms in STP. Labels are provided in the leftmost column.
• Figure 5: Illustration of the binary adding task, designed to test the ability of SNN models to capture long-range dependencies. The sequence length in this task is adjustable, allowing flexibility in controlling the task's difficulty.