6 papers
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…
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…
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…
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…
PASCAL: Precise and Efficient ANN- SNN Conversion using Spike Accumulation and Adaptive Layerwise Activation
Pranav Ramesh, Gopalakrishnan Srinivasan
Spiking Neural Networks (SNNs) have been put forward as an energy-efficient alternative to Artificial Neural Networks (ANNs) since they perform sparse Accumulate operations instead…
QuAKE: Speeding up Model Inference Using Quick and Approximate Kernels for Exponential Non-Linearities
Sai Kiran Narayanaswami, Gopalakrishnan Srinivasan, Balaraman Ravindran
As machine learning gets deployed more and more widely, and model sizes continue to grow, improving computational efficiency during model inference has become a key challenge. In m…