$SpikePack$: Enhanced Information Flow in Spiking Neural Networks with High Hardware Compatibility
Guobin Shen, Jindong Li, Tenglong Li, Dongcheng Zhao, and Yi Zeng

TL;DR
${SpikePack}$ is a novel neuron model that enhances information flow in SNNs, enabling efficient GPU processing, better accuracy, and hardware compatibility for diverse applications.
Contribution
Introduces ${SpikePack}$, a neuron model with constant complexity that improves information transmission, supports efficient parallel processing, and enables high-performance SNN deployment across hardware.
Findings
Achieves significant accuracy improvements in image tasks.
Enables efficient GPU-based SNN processing.
Demonstrates cross-platform hardware compatibility.
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 , a neuron model designed to reduce transmission loss while preserving essential features like membrane potential reset and leaky integration. achieves constant 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…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Neural Networks and Applications · Neural dynamics and brain function
MethodsSpiking Neural Networks
