Hardware implementation of memristor-based artificial neural networks
Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …
techniques, which rely on networks of connected simple computing units operating in …
RAELLA: Reforming the arithmetic for efficient, low-resolution, and low-loss analog PIM: No retraining required!
Processing-In-Memory (PIM) accelerators have the potential to efficiently run Deep Neural
Network (DNN) inference by reducing costly data movement and by using resistive RAM …
Network (DNN) inference by reducing costly data movement and by using resistive RAM …
Graphene-Based Artificial Dendrites for Bio-Inspired Learning in Spiking Neuromorphic Systems
Analog neuromorphic computing systems emulate the parallelism and connectivity of the
human brain, promising greater expressivity and energy efficiency compared to those of …
human brain, promising greater expressivity and energy efficiency compared to those of …
An accurate, error-tolerant, and energy-efficient neural network inference engine based on SONOS analog memory
We demonstrate SONOS (silicon-oxide-nitride-oxide-silicon) analog memory arrays that are
optimized for neural network inference. The devices are fabricated in a 40nm process and …
optimized for neural network inference. The devices are fabricated in a 40nm process and …
Bringing in-sensor intelligence in radiation detectors: a short review
M Carminati, S Di Giacomo, M Ronchi… - IEEE EUROCON …, 2023 - ieeexplore.ieee.org
Technological trends, challenges and solutions recently proposed in the literature to embed
intelligence, in particular machine learning, into solid-state sensors and radiation detectors …
intelligence, in particular machine learning, into solid-state sensors and radiation detectors …
CiMLoop: A Flexible, Accurate, and Fast Compute-In-Memory Modeling Tool
Compute-In-Memory (CiM) is a promising solution to accelerate Deep Neural Networks
(DNNs) as it can avoid energy-intensive DNN weight movement and use memory arrays to …
(DNNs) as it can avoid energy-intensive DNN weight movement and use memory arrays to …
Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materials
The Abisko project aims to develop an energy-efficient spiking neural network (SNN)
computing architecture and software system capable of autonomous learning and operation …
computing architecture and software system capable of autonomous learning and operation …
Neuromorphic Hebbian learning with magnetic tunnel junction synapses
P Zhou, AJ Edwards, FB Mancoff, S Aggarwal… - arXiv preprint arXiv …, 2023 - arxiv.org
Neuromorphic computing aims to mimic both the function and structure of biological neural
networks to provide artificial intelligence with extreme efficiency. Conventional approaches …
networks to provide artificial intelligence with extreme efficiency. Conventional approaches …
The Impact of Analog-to-Digital Converter Architecture and Variability on Analog Neural Network Accuracy
M Spear, JE Kim, CH Bennett, S Agarwal… - IEEE Journal on …, 2023 - ieeexplore.ieee.org
The analog-to-digital converter (ADC) is not only a key component in analog in-memory
computing (IMC) accelerators but also a bottleneck for the efficiency and accuracy of these …
computing (IMC) accelerators but also a bottleneck for the efficiency and accuracy of these …