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Memristive and CMOS devices for neuromorphic computing
Neuromorphic computing has emerged as one of the most promising paradigms to
overcome the limitations of von Neumann architecture of conventional digital processors …
overcome the limitations of von Neumann architecture of conventional digital processors …
Challenges and trends of nonvolatile in-memory-computation circuits for AI edge devices
JM Hung, CJ Jhang, PC Wu, YC Chiu… - IEEE Open Journal of …, 2021 - ieeexplore.ieee.org
Nonvolatile memory (NVM)-based computing-in-memory (nvCIM) is a promising candidate
for artificial intelligence (AI) edge devices to overcome the latency and energy consumption …
for artificial intelligence (AI) edge devices to overcome the latency and energy consumption …
A survey of neuromorphic computing and neural networks in hardware
CD Schuman, TE Potok, RM Patton, JD Birdwell… - ar** nonvolatile memory-enabled computing chips for intelligent edge devices
Under the von Neumann computing architecture, the edge devices used for artificial
intelligence (AI) and the Internet of Things (IoTs) are limited in terms of latency and energy …
intelligence (AI) and the Internet of Things (IoTs) are limited in terms of latency and energy …
Demonstration of hybrid CMOS/RRAM neural networks with spike time/rate-dependent plasticity
Neural networks with resistive-switching memory (RRAM) synapses can mimic learning and
recognition in the human brain, thus overcoming the major limitations of von Neumann …
recognition in the human brain, thus overcoming the major limitations of von Neumann …
SBSNN: Stochastic-bits enabled binary spiking neural network with on-chip learning for energy efficient neuromorphic computing at the edge
In this work, we propose stochastic Binary Spiking Neural Network (sBSNN) composed of
stochastic spiking neurons and binary synapses (stochastic only during training) that …
stochastic spiking neurons and binary synapses (stochastic only during training) that …