Fine-Tuning Surrogate Gradient Learning for Optimal Hardware Performance in Spiking Neural Networks

The highly sparse activations in Spiking Neural Networks (SNNs) can provide tremendous energy efficiency benefits when carefully exploited in hardware. The behavior of sparsity in SNNs is uniquely shaped by the dataset and training hyperparameters. This work reveals novel insights into the impacts of training on hardware performance. Specifically, we explore the trade-offs between model accuracy and hardware efficiency. We focus on three key hyperparameters: surrogate gradient functions, beta, and membrane threshold. Results on an FPGA-based hardware platform show that the fast sigmoid surrogate function yields a lower firing rate with similar accuracy compared to the arctangent surrogate on the SVHN dataset. Furthermore, by cross-sweeping the beta and membrane threshold hyperparameters, we can achieve a 48% reduction in hardware-based inference latency with only 2.88% trade-off in inference accuracy compared to the default setting. Overall, this study highlights the importance of fine-tuning model hyperparameters as crucial for designing efficient SNN hardware accelerators, evidenced by the fine-tuned model achieving a 1.72x improvement in accelerator efficiency (FPS/W) compared to the most recent work.

jazznet: A Dataset of Fundamental Piano Patterns for Music Audio Machine Learning Research

This paper introduces the jazznet Dataset, a dataset of fundamental jazz piano music patterns for developing machine learning (ML) algorithms in music information retrieval (MIR). The dataset contains 162520 labeled piano patterns, including chords, arpeggios, scales, and chord progressions with their inversions, resulting in more than 26k hours of audio and a total size of 95GB. The paper explains the dataset's composition, creation, and generation, and presents an open-source Pattern Generator using a method called Distance-Based Pattern Structures (DBPS), which allows researchers to easily generate new piano patterns simply by defining the distances between pitches within the musical patterns. We demonstrate that the dataset can help researchers benchmark new models for challenging MIR tasks, using a convolutional recurrent neural network (CRNN) and a deep convolutional neural network. The dataset and code are available via: https://github.com/tosiron/jazznet.