211 citations · 530 across the 27 of their papers we have counts for
10 papers · 1 filter
Autonomous Driving with Spiking Neural Networks
Rui-Jie Zhu, Ziqing Wang, Leilani Gilpin +1
Autonomous driving demands an integrated approach that encompasses perception, prediction, and planning, all while operating under strict energy constraints to enhance scalability…
SQUAT: Stateful Quantization-Aware Training in Recurrent Spiking Neural Networks
Sreyes Venkatesh, Razvan Marinescu, Jason K. Eshraghian
Weight quantization is used to deploy high-performance deep learning models on resource-limited hardware, enabling the use of low-precision integers for storage and computation. Sp…
Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing
Jens E. Pedersen, Steven Abreu, Matthias Jobst +12
Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine…
To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration
Fabrizio Ottati, Chang Gao, Qinyu Chen +4
As deep learning models scale, they become increasingly competitive from domains spanning from computer vision to natural language processing; however, this happens at the expense…
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…
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…
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…
Training Spiking Neural Networks Using Lessons From Deep Learning
Jason K. Eshraghian, Max Ward, Emre Neftci +6
The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the futur…