211 citations · 530 across the 27 of their papers we have counts for
9 papers · 1 filter
Intelligence Processing Units Accelerate Neuromorphic Learning
Pao-Sheng Vincent Sun, Alexander Titterton, Anjlee Gopiani +4
Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency when performing inference with deep learning workloads. Erro…
Spiking neural networks for nonlinear regression
Alexander Henkes, Jason K. Eshraghian, Henning Wessels
Spiking neural networks, also often referred to as the third generation of neural networks, carry the potential for a massive reduction in memory and energy consumption over tradit…
Side-channel attack analysis on in-memory computing architectures
Ziyu Wang, Fan-hsuan Meng, Yongmo Park +2
In-memory computing (IMC) systems have great potential for accelerating data-intensive tasks such as deep neural networks (DNNs). As DNN models are generally highly proprietary, th…
Gradient-based Neuromorphic Learning on Dynamical RRAM Arrays
Peng Zhou, Jason K. Eshraghian, Dong-Uk Choi +2
We present MEMprop, the adoption of gradient-based learning to train fully memristive spiking neural networks (MSNNs). Our approach harnesses intrinsic device dynamics to trigger n…
SPICEprop: Backpropagating Errors Through Memristive Spiking Neural Networks
Peng Zhou, Jason K. Eshraghian, Dong-Uk Choi +1
We present a fully memristive spiking neural network (MSNN) consisting of novel memristive neurons trained using the backpropagation through time (BPTT) learning rule. Gradient des…
A Fully Memristive Spiking Neural Network with Unsupervised Learning
Peng Zhou, Dong-Uk Choi, Jason K. Eshraghian +1
We present a fully memristive spiking neural network (MSNN) consisting of physically-realizable memristive neurons and memristive synapses to implement an unsupervised Spiking Time…
Navigating Local Minima in Quantized Spiking Neural Networks
Jason K. Eshraghian, Corey Lammie, Mostafa Rahimi Azghadi +1
Spiking and Quantized Neural Networks (NNs) are becoming exceedingly important for hyper-efficient implementations of Deep Learning (DL) algorithms. However, these networks face ch…
The fine line between dead neurons and sparsity in binarized spiking neural networks
Jason K. Eshraghian, Wei D. Lu
Spiking neural networks can compensate for quantization error by encoding information either in the temporal domain, or by processing discretized quantities in hidden states of hig…
Design Space Exploration of Dense and Sparse Mapping Schemes for RRAM Architectures
Corey Lammie, Jason K. Eshraghian, Chenqi Li +4
The impact of device and circuit-level effects in mixed-signal Resistive Random Access Memory (RRAM) accelerators typically manifest as performance degradation of Deep Learning (DL…