Open-loop analog programmable electrochemical memory array
P Chen, F Liu, P Lin, P Li, Y Xiao, B Zhang… - Nature …, 2023 - nature.com
Emerging memories have been developed as new physical infrastructures for hosting neural
networks owing to their low-power analog computing characteristics. However, accurately …
networks owing to their low-power analog computing characteristics. However, accurately …
In situ Parallel Training of Analog Neural Network Using Electrochemical Random-Access Memory
In-memory computing based on non-volatile resistive memory can significantly improve the
energy efficiency of artificial neural networks. However, accurate in situ training has been …
energy efficiency of artificial neural networks. However, accurate in situ training has been …
Digital biologically plausible implementation of binarized neural networks with differential hafnium oxide resistive memory arrays
The brain performs intelligent tasks with extremely low energy consumption. This work takes
its inspiration from two strategies used by the brain to achieve this energy efficiency: the …
its inspiration from two strategies used by the brain to achieve this energy efficiency: the …
CMOS-compatible electrochemical synaptic transistor arrays for deep learning accelerators
In-memory computing architectures based on memristive crossbar arrays could offer higher
computing efficiency than traditional hardware in deep learning applications. However, the …
computing efficiency than traditional hardware in deep learning applications. However, the …
Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing
Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional
computing through parallel programming and readout of artificial neural network weights in …
computing through parallel programming and readout of artificial neural network weights in …
Neuro-inspired computing with emerging nonvolatile memorys
S Yu - Proceedings of the IEEE, 2018 - ieeexplore.ieee.org
This comprehensive review summarizes state of the art, challenges, and prospects of the
neuro-inspired computing with emerging nonvolatile memory devices. First, we discuss the …
neuro-inspired computing with emerging nonvolatile memory devices. First, we discuss the …
Neuro-inspired computing chips
The rapid development of artificial intelligence (AI) demands the rapid development of
domain-specific hardware specifically designed for AI applications. Neuro-inspired …
domain-specific hardware specifically designed for AI applications. Neuro-inspired …
Metal-oxide based, CMOS-compatible ECRAM for deep learning accelerator
We demonstrate a CMOS-compatible, metal-oxide based Electro-Chemical Random-Access
Memory (MO-ECRAM) for high-speed, low-power neuromorphic computing. The device …
Memory (MO-ECRAM) for high-speed, low-power neuromorphic computing. The device …
Dielectric-engineered high-speed, low-power, highly reliable charge trap flash-based synaptic device for neuromorphic computing beyond inference
The coming of the big-data era brought a need for power-efficient computing that cannot be
realized in the Von Neumann architecture. Neuromorphic computing which is motivated by …
realized in the Von Neumann architecture. Neuromorphic computing which is motivated by …
One transistor one electrolyte‐gated transistor based spiking neural network for power‐efficient neuromorphic computing system
Neuromorphic computing powered by spiking neural networks (SNN) provides a powerful
and efficient information processing paradigm. To harvest the advantage of SNNs, compact …
and efficient information processing paradigm. To harvest the advantage of SNNs, compact …