Nov 18, 2018 · These findings show that this variation does not have a serious impact on MNIST score with 1000 steps in the weight range (conductance range).

Dec 13, 2018 · We also observed that at least a less than 10% of variation in conductance update for each synaptic device is required for achieving comparable ...

This work investigated the analysis using simulations focusing on a spiking neural network (SNN)-based restricted Boltzmann machine (RBM) and revealed that ...

In extremely energy-efficient neuromorphic computing using analog non-volatile memory (NVM) devices, device variability arises due to process.

We investigated the analysis using simulations focusing on a spiking neural network (SNN)-based restricted Boltzmann machine (RBM). MNIST dataset simulation ...

It adjusts synaptic weights, delays, and time constants, and neurons' thresholds in output and hidden layers. It guarantees convergence to minimum error point, ...

In extremely energy-efficient neuromorphic computing using analog non-volatile memory (NVM) devices, device variability arises due to process variation and ...

Jun 10, 2024 · Analysis of Effect of Weight Variation on SNN Chip with PCM-Refresh Method. ... NVM Weight Variation Impact on Analog Spiking Neural Network Chip.

Jun 1, 2021 · NVM Weight Variation Impact on Analog Spiking Neural Network Chip. In extremely energy-efficient neuromorphic computing using analog non- ...

Jul 18, 2023 · In this work, we propose MaxPool with temporal multiplexing for Spiking CNNs (SCNNs), which is amenable for implementation in mixed-signal circuits.