Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Wei. D. Lu"'
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-13 (2024)
Abstract Cutting-edge humanoid machine vision merely mimics human systems and lacks polarimetric functionalities that convey the information of navigation and authentic images. Interspecies-chimera vision reserving multiple hosts’ capacities will l
Externí odkaz:
https://doaj.org/article/1229d876681740aab5a7d3fc6f8e8c1d
Publikováno v:
Advanced Intelligent Systems, Vol 5, Iss 12, Pp n/a-n/a (2023)
Analog compute‐in‐memory (CIM) systems are promising candidates for deep neural network (DNN) inference acceleration. However, as the use of DNNs expands, protecting user input privacy has become increasingly important. Herein, a potential securi
Externí odkaz:
https://doaj.org/article/d194bc0b624c468db12678d4ea2e2ce6
Publikováno v:
Neuromorphic Computing and Engineering, Vol 4, Iss 3, p 030201 (2024)
Externí odkaz:
https://doaj.org/article/e77003582d1a4cb78a1ee0160b7693c9
Autor:
Pao-Sheng Vincent Sun, Alexander Titterton, Anjlee Gopiani, Tim Santos, Arindam Basu, Wei D Lu, Jason K Eshraghian
Publikováno v:
Neuromorphic Computing and Engineering, Vol 4, Iss 1, p 014004 (2024)
Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency when performing inference with deep learning workloads. Error backpropagation is presently regarded as the most effective method f
Externí odkaz:
https://doaj.org/article/ad66fc47593541f39aeef39898bbddc2
Publikováno v:
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Vol 8, Iss 2, Pp 93-101 (2022)
As more cloud computing resources are used for machine learning training and inference processes, privacy-preserving techniques that protect data from revealing at the cloud platforms attract increasing interest. Homomorphic encryption (HE) is one of
Externí odkaz:
https://doaj.org/article/ae04abe2369a4ef8a0875a5097ca1985
Publikováno v:
Advanced Intelligent Systems, Vol 4, Iss 11, Pp n/a-n/a (2022)
Network features found in the brain may help implement more efficient and robust neural networks. Spiking neural networks (SNNs) process spikes in the spatiotemporal domain and can offer better energy efficiency than deep neural networks. However, mo
Externí odkaz:
https://doaj.org/article/c031757ce4a84357ab995b89c8e558a9
Publikováno v:
Advanced Intelligent Systems, Vol 4, Iss 8, Pp n/a-n/a (2022)
In analog in‐memory computing systems based on nonvolatile memories such as resistive random‐access memory (RRAM), neural network models are often trained offline and then the weights are programmed onto memory devices as conductance values. The
Externí odkaz:
https://doaj.org/article/350167ba1f1d481ab3ce6c5ff2adea55
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
Designing energy efficient artificial neural networks for real-time analysis remains a challenge. Here, the authors report the development of a perovskite halide (CsPbI3) memristor-based Reservoir Computing system for real-time recognition of neural
Externí odkaz:
https://doaj.org/article/540a4467b1ba426ca2b44bbe7445bba3
Autor:
Justin M. Correll, Vishishtha Bothra, Fuxi Cai, Yong Lim, Seung Hwan Lee, Seungjong Lee, Wei D. Lu, Zhengya Zhang, Michael P. Flynn
Publikováno v:
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Vol 6, Iss 1, Pp 36-44 (2020)
Analog compute-in-memory with resistive random access memory (RRAM) devices promises to overcome the data movement bottleneck in data-intensive artificial intelligence (AI) and machine learning. RRAM crossbar arrays improve the efficiency of vector-m
Externí odkaz:
https://doaj.org/article/3d1d7a52eebe4f4f88627fff7858e676
Publikováno v:
Advanced Intelligent Systems, Vol 3, Iss 8, Pp n/a-n/a (2021)
The advances of neural recording techniques have fostered rapid growth of the number of simultaneously recorded neurons, opening up new possibilities to investigate the interactions and dynamics inside neural circuitry. The high recording channel cou
Externí odkaz:
https://doaj.org/article/b3d9393c5aba4b6095abed55734be420