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07/18/2023
Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference
Analog In-Memory Computing (AIMC) is a promising approach to reduce the ...
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05/17/2023
AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing
The advancement of Deep Learning (DL) is driven by efficient Deep Neural...
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06/20/2022
Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks
During the past two decades, epileptic seizure detection and prediction ...
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05/13/2022
Toward A Formalized Approach for Spike Sorting Algorithms and Hardware Evaluation
Spike sorting algorithms are used to separate extracellular recordings 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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01/18/2022
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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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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02/17/2021
Towards Memristive Deep Learning Systems for Real-time Mobile Epileptic Seizure Prediction
The unpredictability of seizures continues to distress many people with ...
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01/08/2020
Training Progressively Binarizing Deep Networks Using FPGAs
While hardware implementations of inference routines for Binarized Neura...
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10/14/2019
Variation-aware Binarized Memristive Networks
The quantization of weights to binary states in Deep Neural Networks (DN...
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05/15/2019