arXiv AI By Riadul Islam, Joey Mule, Dhandeep Challagundla, Shahmir Rizvi, Sean Carson, Rachit Saini

LiteEvent-AE: Lightweight Autoencoder for Event-Based Vision on Low-Latency Energy-Constrained Edge Devices

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arXiv AI
1d ago

ANTShapes Benchmarking Datasets for Event-Based Neuromorphic Object Classification

The paper introduces four new event‑based vision datasets created with the ANTShapes simulation tool, designed to support object classification research using spiking neural networks (SNNs). These datasets vary in difficulty and are benchmarked against established spiking datasets such as N‑MNIST, CIFAR10‑DVS, DVSGesture, and POKER‑DVS using a convolutional SNN. The work provides detailed, high‑quality datasets for future experiments and validates ANTShapes as a suitable tool for generating event‑based vision data.

By M. Middleton, H. Kayan, B. Sen Bhattacharya, T. Ali, E. Baikas, M. Vousden, C. Perera, O. Rhodes, E. Gheorghiu, M. A. Trefzer
arXiv AI
Jul 28

The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing

arXiv:2607. 24396v1 Announce Type: cross Abstract: In deep learning, efficiency gets more and more important to compensate for the ongoing growth in model sizes and applications.

By Stefan Scholze, Johannes Partzsch, Sebastian H\"oppner, Florian Kelber, Andreas Dixius, Marco Stolba, Sirine Arfa, Marc Berthel, Georg Ellguth, Jim Garside, Hector A. Gonzalez, Stephan Hartmann, Thomas Kiel-Hocker, Dongwei Hu, Matthias Jobst, Khaleelulla Khan Nazeer, Tim Langer, Chen Liu, Gengting Liu, Matthias Lohrmann, Mantas Mikaitis, Felix Neum\"arker, Amirhossein Rostami, Stefan Schiefer, Tilo Schubert, Delong Shang, Bernhard Vogginger, Yexin Yan, Steve Furber, Christian Mayr
arXiv Machine Learning
3d ago

Low-Latency Activation-Regularized Sparse Neural Operators with Distillation Assistance Towards Real-Time Edge-Deployable Virtual Sensing

The paper introduces the Sparse-Activation-ReLU (SAR) layer, a single‑step neural operator that promotes activation sparsity without surrogate‑gradient training and is compatible with event‑based computing. In a trunk‑based NOMAD architecture, SAR improves the combined Latency‑Error‑Energy (LEE) metric by over fivefold compared to Variable Spiking Neuron (VSN) and Leaky Integrate‑and‑Fire (LIF) models. Additional techniques such as synthetic knowledge distillation, a ReLU‑based spiking loss, and graph‑neighbor thresholding further reduce LEE and L2 error on the Heat Exchanger dataset, advancing energy‑efficient virtual sensing for edge deployment.

By William Howes, Farid Ahmed, Syed Bahauddin Alam