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05/23/2023
Bulk-Switching Memristor-based Compute-In-Memory Module for Deep Neural Network Training
The need for deep neural network (DNN) models with higher performance an...
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04/13/2023
PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators
Analog compute-in-memory (CIM) accelerators are becoming increasingly po...
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03/19/2023
RN-Net: Reservoir Nodes-Enabled Neuromorphic Vision Sensing Network
Event-based cameras are inspired by the sparse and asynchronous spike re...
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11/19/2022
Intelligence Processing Units Accelerate Neuromorphic Learning
Spiking neural networks (SNNs) have achieved orders of magnitude improve...
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09/06/2022
Side-channel attack analysis on in-memory computing architectures
In-memory computing (IMC) systems have great potential for accelerating ...
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06/26/2022
Gradient-based Neuromorphic Learning on Dynamical RRAM Arrays
We present MEMprop, the adoption of gradient-based learning to train ful...
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02/15/2022
Navigating Local Minima in Quantized Spiking Neural Networks
Spiking and Quantized Neural Networks (NNs) are becoming exceedingly imp...
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01/28/2022
The fine line between dead neurons and sparsity in binarized spiking neural networks
Spiking neural networks can compensate for quantization error by encodin...
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01/18/2022
Design Space Exploration of Dense and Sparse Mapping Schemes for RRAM Architectures
The impact of device and circuit-level effects in mixed-signal Resistive...
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09/27/2021
Training Spiking Neural Networks Using Lessons From Deep Learning
The brain is the perfect place to look for inspiration to develop more e...
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05/14/2021
Hierarchical Architectures in Reservoir Computing Systems
Reservoir computing (RC) offers efficient temporal data processing with ...
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03/11/2021
Memristive Stochastic Computing for Deep Learning Parameter Optimization
Stochastic Computing (SC) is a computing paradigm that allows for the lo...
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12/09/2016