Filtered by: Scheduling × Parallelism × Clear all

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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GF-DiT: Scheduling Parallelism for Diffusion Transformer Serving

Xinwei Qiang, Yifan Hu, Shixuan Sun, Jing Yang 2026-06-14
Transformer GPU Runtime Scheduling × Parallelism × Serving

The problem is that existing Diffusion Transformer (DiT) serving systems use static parallelism for each request, which is inefficient due to heterogeneity across requests, execution stages, and system conditions. GF-DiT introduces a policy-programmable runtime that dynamically adapts parallelism via an asynchronous execution abstraction and group-free collectives for low-overhead online GPU reallocation. Experimental evaluation in vLLM-Omni shows GF-DiT improves throughput by up to 6.01×, reduces mean latency by up to 95%, and lowers SLO violation rates by up to 90% compared to fixed-pipeline execution. This matters because it enables efficient, elastic DiT serving that treats GPU parallelism as a schedulable resource, significantly improving performance and service quality for image and video generation workloads.

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