Results 31 to 40 of about 1,025,950 (315)
AM4: MRAM Crossbar Based CAM/TCAM/ACAM/AP for In-Memory Computing
IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2023 In-memory computing seeks to minimize data movement and alleviate the memory wall by computing in-situ, in the same place that the data is located. One of the key emerging technologies that promises to enable such computing-in-memory is spin-transfer ...
Esteban Garzón +3 more
semanticscholar +1 more source
Memristors—From In‐Memory Computing, Deep Learning Acceleration, and Spiking Neural Networks to the Future of Neuromorphic and Bio‐Inspired Computing [PDF]
Advanced Intelligent Systems, 2020 Machine learning, particularly in the form of deep learning (DL), has driven most of the recent fundamental developments in artificial intelligence (AI).
A. Mehonic +5 more
semanticscholar +1 more source
An Integrated Photorefractive Analog Matrix-Vector Multiplier for Machine Learning
Applied Sciences, 2022 AI is fueling explosive growth in compute demand that traditional digital chip architectures cannot keep up with. Analog crossbar arrays enable power efficient synaptic signal processing with linear scaling on neural network size.
Elger A. Vlieg +4 more
doaj +1 more source
High efficiency coherent optical memory with warm rubidium vapour [PDF]
, 2010 By harnessing aspects of quantum mechanics, communication and information processing could be radically transformed. Promising forms of quantum information technology include optical quantum cryptographic systems and computing using photons for quantum ...
Buchler, B. C. +4 more
core +3 more sources
Memristive devices for computation-in-memory [PDF]
2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2018 CMOS technology and its continuous scaling have made electronics and computers accessible and affordable for almost everyone on the globe; in addition, they have enabled the solutions of a wide range of societal problems and applications. Today, however, both the technology and the computer architectures are facing severe challenges/walls making them ...
Yu, Jintao +4 more
openaire +3 more sources
C3SRAM: An In-Memory-Computing SRAM Macro Based on Robust Capacitive Coupling Computing Mechanism
IEEE Journal of Solid-State Circuits, 2020 This article presents C3SRAM, an in-memory-computing SRAM macro. The macro is an SRAM module with the circuits embedded in bitcells and peripherals to perform hardware acceleration for neural networks with binarized weights and activations.
Zhewei Jiang +3 more
semanticscholar +1 more source
ACS Nano, 2022 In-memory computing featuring a radical departure from the von Neumann architecture is promising to substantially reduce the energy and time consumption for data-intensive computation.
Zheng‐Dong Luo +9 more
semanticscholar +1 more source
A Heterogeneous In-Memory Computing Cluster for Flexible End-to-End Inference of Real-World Deep Neural Networks [PDF]
IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2022 Deployment of modern TinyML tasks on small battery-constrained IoT devices requires high computational energy efficiency. Analog In-Memory Computing (IMC) using non-volatile memory (NVM) promises major efficiency improvements in deep neural network (DNN)
Angelo Garofalo +6 more
semanticscholar +1 more source
Truss Decomposition in Massive Networks [PDF]
, 2012 The k-truss is a type of cohesive subgraphs proposed recently for the study of networks. While the problem of computing most cohesive subgraphs is NP-hard, there exists a polynomial time algorithm for computing k-truss. Compared with k-core which is also
Cheng, James, Wang, Jia
core +2 more sources
A Programmable Heterogeneous Microprocessor Based on Bit-Scalable In-Memory Computing
IEEE Journal of Solid-State Circuits, 2020 In-memory computing (IMC) addresses the cost of accessing data from memory in a manner that introduces a tradeoff between energy/throughput and computation signal-to-noise ratio (SNR).
Hongyang Jia +4 more
semanticscholar +1 more source