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07/24/2023
Neuromorphic Neuromodulation: Towards the next generation of on-device AI-revolution in electroceuticals
Neuromodulation techniques have emerged as promising approaches for trea...
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06/27/2023
To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration
As deep learning models scale, they become increasingly competitive from...
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06/22/2023
Memristive Reservoirs Learn to Learn
Memristive reservoirs draw inspiration from a novel class of neuromorphi...
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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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02/27/2023
SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks
As the size of large language models continue to scale, so does the comp...
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02/02/2023
OpenSpike: An OpenRAM SNN Accelerator
This paper presents a spiking neural network (SNN) accelerator made usin...
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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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10/06/2022
Spiking neural networks for nonlinear regression
Spiking neural networks, also often referred to as the third generation ...
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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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03/02/2022
SPICEprop: Backpropagating Errors Through Memristive Spiking Neural Networks
We present a fully memristive spiking neural network (MSNN) consisting o...
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03/02/2022
A Fully Memristive Spiking Neural Network with Unsupervised Learning
We present a fully memristive spiking neural network (MSNN) consisting o...
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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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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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02/07/2021
CrossStack: A 3-D Reconfigurable RRAM Crossbar Inference Engine
Deep neural network inference accelerators are rapidly growing in import...
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06/22/2019
