用于混合信号脉冲神经网络设计空间探索的硬件感知开源框架
A Hardware-Aware Open-Source Framework for Design Space Exploration of Mixed-Signal Spiking Neural Networks
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中文总结 AI 辅助
该研究针对边缘节能神经形态计算，提出开源硬件感知仿真框架用于混合信号SNN设计空间探索，支持多种模型与突触，纳入器件非线性优化参数，经基准测试报告多指标，助力跨层探索与配置确定。
中文摘要 AI 辅助
边缘的节能神经形态计算需要能捕捉混合信号脉冲神经网络（SNN）硬件非理想行为且支持系统级设计探索的仿真工具。本文提出一个用于混合信号SNN的开源硬件感知仿真框架，能对神经元、突触和架构选择进行对比分析。该框架支持多种神经元模型，如泄漏积分发放（LIF）、霍奇金-赫胥黎（HH）和轴突丘（AH），以及基于浮栅晶体管和忆阻器的非易失性模拟突触。通过将器件级非线性直接纳入基于PyTorch的训练和推理，可优化物理突触参数而非理想化抽象权重。在标准神经形态基准测试上评估该框架，报告分类准确率及硬件相关指标，如硅面积、功耗和量化敏感度。这些功能实现跨层设计空间探索，有助于确定最满足特定应用精度、能效、面积和硬件保真度约束的神经元-突触配置。
英文摘要
Energy-efficient neuromorphic computing at the edge requires simulation tools that can capture the non-ideal behavior of mixed-signal spiking neural network (SNN) hardware while supporting system-level design exploration. This work presents an open-source hardware-aware simulation framework for mixed-signal SNNs that enables comparative analysis across neuron, synapse and architecture choices. The framework supports multiple neuron models, including Leaky Integrate-and-Fire (LIF), Hodgkin-Huxley (HH) and Axon-Hillock (AH), together with non-volatile analog synapses based on floating-gate transistors and ReRAM devices. By incorporating device-level nonlinearities directly into PyTorch-based training and inference, the tool enables optimization of physical synaptic parameters rather than idealized abstract weights. The framework is evaluated on standard neuromorphic benchmarks, including N-MNIST, DVS Gesture and Spiking Heidelberg Digits (SHD). For each model dataset configuration, it reports classification accuracy together with hardware-oriented metrics such as silicon area, power consumption and quantization sensitivity. These capabilities enable cross-layer design space exploration and help identify neuron-synapse configurations that best satisfy application-specific constraints on accuracy, energy efficiency, area and hardware fidelity.
