Recent advances and future prospects for memristive materials, devices, and systems
Memristive technology has been rapidly emerging as a potential alternative to traditional
CMOS technology, which is facing fundamental limitations in its development. Since oxide …
CMOS technology, which is facing fundamental limitations in its development. Since oxide …
A comprehensive review of advanced trends: from artificial synapses to neuromorphic systems with consideration of non-ideal effects
A neuromorphic system is composed of hardware-based artificial neurons and synaptic
devices, designed to improve the efficiency of neural computations inspired by energy …
devices, designed to improve the efficiency of neural computations inspired by energy …
Material to system-level benchmarking of CMOS-integrated RRAM with ultra-fast switching for low power on-chip learning
Analog hardware-based training provides a promising solution to developing state-of-the-art
power-hungry artificial intelligence models. Non-volatile memory hardware such as resistive …
power-hungry artificial intelligence models. Non-volatile memory hardware such as resistive …
Using the IBM analog in-memory hardware acceleration kit for neural network training and inference
ABSTRACT Analog In-Memory Computing (AIMC) is a promising approach to reduce the
latency and energy consumption of Deep Neural Network (DNN) inference and training …
latency and energy consumption of Deep Neural Network (DNN) inference and training …
Fast and robust analog in-memory deep neural network training
Analog in-memory computing is a promising future technology for efficiently accelerating
deep learning networks. While using in-memory computing to accelerate the inference …
deep learning networks. While using in-memory computing to accelerate the inference …
SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables
I Al-Hussaini, CS Mitchell - Bioengineering, 2023 - mdpi.com
This work presents SeizFt—a novel seizure detection framework that utilizes machine
learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by …
learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by …
Analog Resistive Switching Devices for Training Deep Neural Networks with the Novel Tiki-Taka Algorithm
A critical bottleneck for the training of large neural networks (NNs) is communication with off-
chip memory. A promising mitigation effort consists of integrating crossbar arrays of …
chip memory. A promising mitigation effort consists of integrating crossbar arrays of …
Memristor-based hardware accelerators for artificial intelligence
Satisfying the rapid evolution of artificial intelligence (AI) algorithms requires exponential
growth in computing resources, which, in turn, presents huge challenges for deploying AI …
growth in computing resources, which, in turn, presents huge challenges for deploying AI …
Performance and utility trade-off in interpretable sleep staging
I Al-Hussaini, CS Mitchell - arXiv preprint arXiv:2211.03282, 2022 - arxiv.org
Recent advances in deep learning have led to the development of models approaching the
human level of accuracy. However, healthcare remains an area lacking in widespread …
human level of accuracy. However, healthcare remains an area lacking in widespread …
ReSta: Recovery of Accuracy During Training of Deep Learning Models in a 14-nm Technology-Based ReRAM Array
In this article, we propose an electrical bias technique to recover the accuracy of a degraded-
based resistive random access memory (ReRAM) array in deep neural network (DNN) …
based resistive random access memory (ReRAM) array in deep neural network (DNN) …