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.
V .et al.Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations.Proc
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A neuron-astrocyte network with dual-timescale memory reduces median path lengths up to sixfold in partially observable grid-world navigation tasks.
Clockless FPGA circuits produce autonomous spiking neuron networks that achieve competitive audio classification accuracy with significantly lower power than conventional digital implementations.
No single post-Moore technology replaces current HPC for plasma simulations, but FPGA-class accelerators offer near-term kernel offload, non-von Neumann architectures medium-term operator acceleration, and quantum computing long-term potential for warm dense matter microphysics.
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Dual-Timescale Memory in a Spiking Neuron-Astrocyte Network for Efficient Navigation
A neuron-astrocyte network with dual-timescale memory reduces median path lengths up to sixfold in partially observable grid-world navigation tasks.