SupraSNN introduces a superscalar-inspired SNN accelerator with decoupled synapse and neuron units, multi-cast/merge trees, and partitioning/scheduling that reports 47.6% lower latency and 5.6x better energy efficiency than prior FPGA SNN designs on MNIST and SHD tasks.
(5) Strakosas, X.; Bongo, M.; Owens, R
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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First-principles calculations show BBL exhibits an alternating odd/even pattern of electronic gap narrowing upon redox charging, with bell-shaped conductivity arising from its electronic structure and supramolecular organization.
citing papers explorer
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SupraSNN: Exploiting Synapse-Level Parallelism in Spiking Neural Network Accelerators through Co-Optimized Mapping and Scheduling
SupraSNN introduces a superscalar-inspired SNN accelerator with decoupled synapse and neuron units, multi-cast/merge trees, and partitioning/scheduling that reports 47.6% lower latency and 5.6x better energy efficiency than prior FPGA SNN designs on MNIST and SHD tasks.
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Exploring the Origins of Anti-Ambipolarity in BBL Polymer: Links to Redox Chemistry, Electronic Structure, and Structural Dynamics
First-principles calculations show BBL exhibits an alternating odd/even pattern of electronic gap narrowing upon redox charging, with bell-shaped conductivity arising from its electronic structure and supramolecular organization.