Home /Research /A low-power, high-accuracy with fully on-chip ternary weight hardware architecture for Deep Spiking Neural Networks
LEARNING

A low-power, high-accuracy with fully on-chip ternary weight hardware architecture for Deep Spiking Neural Networks

Duy-Anh Nguyen, Xuan‐Tu Tran, Khanh N. Dang, Francesca Iacopi

Year
2022
Citations
12

Keywords

Computer scienceChipPower (physics)Computer hardwareEmbedded systemArchitectureArtificial neural networkSpiking neural networkTernary operationComputer architecture

Related papers

Browse all LEARNING papers