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SupraSNN: Exploiting Synapse-Level Parallelism in Spiking Neural Network Accelerators through Co-Optimized Mapping and Scheduling

Seyed Sadra Ghavami, Mohammad Hossein Nikkhah, Mohammad Rasoul Roshanshah, Saeed Safari 2026-06-14
Parallelism Neural Network × Accelerator Scheduling

The problem is that deploying Spiking Neural Networks (SNNs) on hardware is limited by the challenge of managing massive parallelism, analogous to the historical barrier of serial execution in processors. The method introduces SupraSNN, a superscalar-inspired hardware-software co-design framework that treats synaptic events as parallelizable micro-operations, using a Multi-Cast Tree, parallel Synapse Processing Units, and a Merge Tree with a unified Neuron Unit. Experimental evidence shows that on a Xilinx Zynq XC7Z020 FPGA, SupraSNN achieves 149 μs inference latency and 0.025 mJ per image for MNIST (93.44% accuracy), delivering 47.6% lower latency and 5.6× better energy efficiency than prior FPGA-based SNN accelerators. This matters because it demonstrates a practical path to high synapse-level parallelism and energy efficiency for SNN deployment, extending to recurrent SNNs on the Spiking Heidelberg Dataset.

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Attention by Synchronization in Coupled Oscillator Networks

Fabio Pasqualetti, Taosha Guo 2026-06-14
Attention Language Model Training Transformer Hardware Network ×

The problem is that softmax attention requires exponentiation and global reduction, which are energy-expensive on von Neumann hardware and lack a natural physical analog. The method replaces softmax with Kuramoto synchronization dynamics, where queries are fixed anchors on a sphere and free oscillators equilibrate to encode attention weights via cosine similarity. Experimental evidence shows that at oscillator dimension 2, oscillator attention outperforms softmax on keyword spotting (+1.00 pp) and subject-verb agreement (+5.27 pp), while on causal language modeling it closes the perplexity gap as dimension increases, from +11.09 to +2.98 on WikiText-2 and from +2.39 to +0.57 on TinyStories. This matters because it provides a mathematically grounded blueprint for accurate attention on energy-constrained physical substrates without requiring exponentiation or global reduction.

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ReSCom: A Reconfigurable Spiking Neural Network Accelerator Using Stochastic Computing

Ali Alipour Fereidani, Mohammad Rasoul Roshanshah, Saeed Safari 2026-06-14
Neural Network × Accelerator

ReSCom addresses the high power and area costs of Spiking Neural Network (SNN) hardware by introducing a reconfigurable accelerator that uses stochastic computing for multiplication while preserving exact fixed-point addition and subtraction. The method employs a unified neuron design supporting IF, LIF, and Synaptic models, enabling runtime trade-offs between accuracy, latency, and energy. On MNIST inference with a Xilinx Artix-7 FPGA, ReSCom achieves 92.80% accuracy at 0.05 mJ per image and 100 MHz, outperforming recent state-of-the-art implementations in energy efficiency. This matters because it demonstrates that stochastic computing can stabilize SNN inference while providing explicit, dynamic control over accuracy-latency-energy trade-offs for resource-constrained edge applications.

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Work Stealing for the 2D-Mesh Topology of Satellite Constellations in Low Earth Orbit

Mia Reitz, Dorian Chenet, Jonas Posner 2026-06-14
High × Performance Computing HPC Compiler Runtime

The problem is that existing Asynchronous Many-Task (AMT) runtimes assume a fully connected network with low, uniform latency, which is invalid for satellite constellations in Low Earth Orbit (LEO) that communicate via a sparse mesh topology. The method proposes a neighbor-only work stealing strategy where workers steal exclusively from directly connected neighbors to avoid multi-hop communication. Experimental evidence on an HPC cluster with an emulated mesh shows the neighbor-only strategy performs within ~2.2% of global stealing on both balanced and irregular workloads, and an analytical model indicates a growing latency advantage with constellation size. This matters because it demonstrates that neighbor-only stealing can match global stealing performance in emulated settings, suggesting it is a viable and potentially preferable approach for adapting AMT to Space Edge Computing (SEC) at scale.

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