Artifact Release for ISPASS 2026 Paper: Surrogates, Spikes, and Sparsity: Performance Analysis and Characterization of SNN Hyperparameters on Hardware Authors: Ilkin Aliyev, Jesus Lopez, Tosiron Adegbija Venue: IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS 2026) Overview This release contains the reproducibility artifacts for our ISPASS 2026 paper, including: Training scripts for spiking neural networks with configurable surrogate gradient functions (Fast Sigmoid, Arctangent, Spike Rate Escape, Stochastic Spike Operator) and neuron models (LIF, Lapicque) FPGA instrumentation platform (SystemVerilog RTL) for cycle-accurate latency and spike count measurement Weight extraction utilities for deploying trained models to hardware Key Results Reproducible Figure 5: Accuracy sensitivity to surrogate gradient functions Figure 6: Hardware inference latency characterization Figure 7: Pareto analysis of neuron configurations (LIF vs. Lapicque) Table II: Benchmarking against prior ASIC/FPGA implementations Requirements Python 3.11, PyTorch 2.2.2, CUDA 12.8 snnTorch 0.7.0, Brevitas 0.10.2, tonic Xilinx Vivado (for hardware simulation) Datasets DVS128-Gesture N-MNIST DVS-CIFAR10 License MIT License Citation @inproceedings{aliyev2026surrogates, title={Surrogates, Spikes, and Sparsity: Performance Analysis and Characterization of SNN Hyperparameters on Hardware}, author={Aliyev, Ilkin and Lopez, Jesus and Adegbija, Tosiron}, booktitle={IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)}, year={2026} }