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Reconfigurable In-Sensor Computing Memristor for Olfactory SNN and Reservoir Hybrid Neuromorphic Computing [PDF]

Research
Traditional gas sensing systems are facing efficiency challenges due to physically separated von Neumann architectures, making the construction of in-sensor computing neuromorphic olfactory systems urgently needed for low-power and low-latency scenarios.
Lin Lu, Jinhao Zhang, Qingxin Chen, Jialin Meng, Yongjin Zou, Yilin Wang, Tianyu Wang   +6 more
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Two-Dimensional MXene-Based Advanced Sensors for Neuromorphic Computing Intelligent Application [PDF]

Nano-Micro Letters
Highlights The latest research progress in the field of MXene-based neuromorphic computing is reviewed. The design strategy of MXene-based neuromorphic devices encompasses multiple factors are summarized, including material selection, circuit integration,
Lin Lu, Bo Sun, Zheng Wang, Jialin Meng, Tianyu Wang   +4 more
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A Review of Nanowire Devices Applied in Simulating Neuromorphic Computing [PDF]

Nanomaterials
With the rapid advancement of artificial intelligence and machine learning technologies, the demand for enhanced device computing capabilities has significantly increased.
Tianci Huang, Yuxuan Wang, Zhihan Jin, Hao Liu, Kaili Wang, Tan Leong Chee, Yi Shi, Shancheng Yan   +7 more
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Electrolyte Gated Transistors for Brain Inspired Neuromorphic Computing and Perception Applications: A Review [PDF]

Nanomaterials
Emerging neuromorphic computing offers a promising and energy-efficient approach to developing advanced intelligent systems by mimicking the information processing modes of the human brain.
Weisheng Wang, Liqiang Zhu
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Is Neuromorphic MNIST Neuromorphic? Analyzing the Discriminative Power of Neuromorphic Datasets in the Time Domain

Frontiers in Neuroscience, 2021
A major characteristic of spiking neural networks (SNNs) over conventional artificial neural networks (ANNs) is their ability to spike, enabling them to use spike timing for coding and efficient computing.
Laxmi R. Iyer, Yansong Chua, Haizhou Li, Haizhou Li   +3 more
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The Intel neuromorphic DNS challenge

Neuromorphic Computing and Engineering, 2023
A critical enabler for progress in neuromorphic computing research is the ability to transparently evaluate different neuromorphic solutions on important tasks and to compare them to state-of-the-art conventional solutions.
Jonathan Timcheck, Sumit Bam Shrestha, Daniel Ben Dayan Rubin, Adam Kupryjanow, Garrick Orchard, Lukasz Pindor, Timothy Shea, Mike Davies   +7 more
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Simulation-based inference for model parameterization on analog neuromorphic hardware

Neuromorphic Computing and Engineering, 2023
The BrainScaleS-2 (BSS-2) system implements physical models of neurons as well as synapses and aims for an energy-efficient and fast emulation of biological neurons.
Jakob Kaiser, Raphael Stock, Eric Müller, Johannes Schemmel, Sebastian Schmitt   +4 more
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CMOS-compatible neuromorphic devices for neuromorphic perception and computing: a review

International Journal of Extreme Manufacturing, 2023
Neuromorphic computing is a brain-inspired computing paradigm that aims to construct efficient, low-power, and adaptive computing systems by emulating the information processing mechanisms of biological neural systems.
Yixin Zhu, Huiwu Mao, Ying Zhu, Xiangjing Wang, Chuanyu Fu, Shuo Ke, Changjin Wan, Qing Wan   +7 more
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Recent Progress of Optoelectronic and All‐Optical Neuromorphic Devices: A Comprehensive Review of Device Structures, Materials, and Applications

Advanced Intelligent Systems, 2021
Conventional von Neumann–based computing systems have inherent limitations such as high hardware complexity, relatively inferior energy efficiency, and low bandwidth.
Seungho Song, Jeehoon Kim, Sung Min Kwon, Jeong‐Wan Jo, Sung Kyu Park, Yong‐Hoon Kim   +5 more
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Editorial: Focus on algorithms for neuromorphic computing

Neuromorphic Computing and Engineering, 2023
Neuromorphic computing provides a promising energy-efficient alternative to von-Neumann-type computing and learning architectures. However, the best neuromorphic hardware is useless without suitable inference and learning algorithms that can fully ...
Robert Legenstein, Arindam Basu, Priyadarshini Panda   +2 more
doaj   +1 more source