Hardware/Software co-design with ADC-Less In-memory Computing Hardware for Spiking Neural Networks

Spiking Neural Networks (SNNs) are bio-plausible models that hold great potential for realizing energy-efficient implementations of sequential tasks on resource-constrained edge devices. However, commercial edge platforms based on standard GPUs are not optimized to deploy SNNs, resulting in high ene...

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Veröffentlicht in:	IEEE transactions on emerging topics in computing 2024-01, Vol.12 (1), p.1-13
Hauptverfasser:	Apolinario, Marco P. E., Kosta, Adarsh Kumar, Saxena, Utkarsh, Roy, Kaushik
Format:	Artikel
Sprache:	eng
Schlagworte:	ADC-Less Co-design Computation Computational modeling Energy efficiency Gesture recognition Hardware HW/SW co-design Image classification in-memory computing Membrane potentials Micromechanical devices Network latency Neural networks Neurons Optical flow (image analysis) Platforms Sense amplifiers Software spiking neural networks Task analysis Task complexity Training
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