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Found 3 papers, 0 papers with code
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.
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Gesture Recognition
on DVS128 Gesture
On-chip learning in a conventional silicon MOSFET based Analog Hardware Neural Network
On-chip learning in a crossbar array based analog hardware Neural Network (NN) has been shown to have major advantages in terms of speed and energy compared to training NN on a traditional computer.
On-chip learning for domain wall synapse based Fully Connected Neural Network
Spintronic devices are considered as promising candidates in implementing neuromorphic systems or hardware neural networks, which are expected to perform better than other existing computing systems for certain data classification and regression tasks.
