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cs.LG2026
Spike-Aware Gradient-Based Mixed-Precision Quantization of Spiking Neural Networks for Vision Applications
Rajshekhar Rakshit, Devgokul B., Rakshitha Kalkura +1
In recent years, Spiking Neural Networks (SNNs) have gained massive attention as an energy-efficient neural computing paradigm due to their event-driven and sparse computing capabi…
cs.AR2026
NeuroFlex: Lossless Element-Level ANN-SNN Co-Execution for Efficient Sparse Inference
Varun Manjunath, Pranav Ramesh, Gopalakrishnan Srinivasan
Sparse DNN accelerators specialize in ANN or SNN execution, leaving energy or latency on the table when workload characteristics vary within a layer. Hybrid accelerator designs tha…
cs.AR2026
APEX: A Dual-Sparsity Accelerator for Precise and Efficient SNN Inference
Devgokul Bawa Venkatesh, Sreeram Radhakrishnan, Rajshekhar Rakshit +1
Spiking Neural Networks (SNNs) have emerged as an energy-efficient alternative to Artificial Neural Networks (ANNs), leveraging sparse accumulate operations in the place of power-h…