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 …
A full spectrum of computing-in-memory technologies
Computing in memory (CIM) could be used to overcome the von Neumann bottleneck and to
provide sustainable improvements in computing throughput and energy efficiency …
provide sustainable improvements in computing throughput and energy efficiency …
A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference
Analogue in-memory computing (AIMC) with resistive memory devices could reduce the
latency and energy consumption of deep neural network inference tasks by directly …
latency and energy consumption of deep neural network inference tasks by directly …
Prospects and applications of photonic neural networks
C Huang, VJ Sorger, M Miscuglio… - … in Physics: X, 2022 - Taylor & Francis
Neural networks have enabled applications in artificial intelligence through machine
learning, and neuromorphic computing. Software implementations of neural networks on …
learning, and neuromorphic computing. Software implementations of neural networks on …
Simulation intelligence: Towards a new generation of scientific methods
The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …
computing, where a motif is an algorithmic method that captures a pattern of computation …
ECRAM materials, devices, circuits and architectures: A perspective
Non‐von‐Neumann computing using neuromorphic systems based on two‐terminal
resistive nonvolatile memory elements has emerged as a promising approach, but its full …
resistive nonvolatile memory elements has emerged as a promising approach, but its full …
Bio‐Inspired 3D Artificial Neuromorphic Circuits
X Liu, F Wang, J Su, Y Zhou… - Advanced Functional …, 2022 - Wiley Online Library
Neuromorphic circuits emulating the bio‐brain functionality via artificial devices have
achieved a substantial scientific leap in the past decade. However, even with the advent of …
achieved a substantial scientific leap in the past decade. However, even with the advent of …
Ferroelectric field effect transistors for electronics and optoelectronics
Ferroelectric materials have shown great value in the modern semiconductor industry and
are considered important function materials due to their high dielectric constant and tunable …
are considered important function materials due to their high dielectric constant and tunable …
Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks
Artificial neural networks have demonstrated superiority over traditional computing
architectures in tasks such as pattern classification and learning. However, they do not …
architectures in tasks such as pattern classification and learning. However, they do not …
Silicon microring synapses enable photonic deep learning beyond 9-bit precision
Deep neural networks (DNNs) consist of layers of neurons interconnected by synaptic
weights. A high bit-precision in weights is generally required to guarantee high accuracy in …
weights. A high bit-precision in weights is generally required to guarantee high accuracy in …