$SpikePack$: Enhanced Information Flow in Spiking Neural Networks with High Hardware Compatibility
Journal:
arXiv
Published Date:
Jan 24, 2025
Abstract
Spiking Neural Networks (SNNs) hold promise for energy-efficient,
biologically inspired computing. We identify substantial informatio loss during
spike transmission, linked to temporal dependencies in traditional Leaky
Integrate-and-Fire (LIF) neuron-a key factor potentially limiting SNN
performance. Existing SNN architectures also underutilize modern GPUs,
constrained by single-bit spike storage and isolated weight-spike operations
that restrict computational efficiency. We introduce ${SpikePack}$, a neuron
model designed to reduce transmission loss while preserving essential features
like membrane potential reset and leaky integration. ${SpikePack}$ achieves
constant $\mathcal{O}(1)$ time and space complexity, enabling efficient
parallel processing on GPUs and also supporting serial inference on existing
SNN hardware accelerators. Compatible with standard Artificial Neural Network
(ANN) architectures, ${SpikePack}$ facilitates near-lossless ANN-to-SNN
conversion across various networks. Experimental results on tasks such as image
classification, detection, and segmentation show ${SpikePack}$ achieves
significant gains in accuracy and efficiency for both directly trained and
converted SNNs over state-of-the-art models. Tests on FPGA-based platforms
further confirm cross-platform flexibility, delivering high performance and
enhanced sparsity. By enhancing information flow and rethinking SNN-ANN
integration, ${SpikePack}$ advances efficient SNN deployment across diverse
hardware platforms.
