Results 41 to 50 of about 931,340 (223)

Flash Memory Array for Efficient Implementation of Deep Neural Networks

Advanced Intelligent Systems, 2021
The advancement of artificial intelligence applications is promoted by developing deep neural networks (DNNs) with increasing sizes and putting forward higher computing power requirements of the processing devices.
Runze Han, Yachen Xiang, Peng Huang, Yihao Shan, Xiaoyan Liu, Jinfeng Kang   +5 more
doaj   +1 more source

CIDAN-XE: Computing in DRAM with Artificial Neurons

Frontiers in Electronics, 2022
This paper presents a DRAM-based processing-in-memory (PIM) architecture, called CIDAN-XE. It contains a novel computing unit called the neuron processing element (NPE).
Gian Singh, Ankit Wagle, Sunil Khatri, Sarma Vrudhula   +3 more
doaj   +1 more source

Exploiting drain-erase scheme in ferroelectric FETs for logic-in-memory

Neuromorphic Computing and Engineering
The conventional computing platforms based on von-Neumann architecture are highly space- and energy-intensive while handling the emerging applications such as AI, ML, and big data. To overcome the von Neumann bottleneck, compact and light-weight logic-in-
Musaib Rafiq, Yogesh Singh Chauhan, Shubham Sahay   +2 more
doaj   +1 more source

A survey on processing-in-memory techniques: Advances and challenges

Memories - Materials, Devices, Circuits and Systems, 2023
Processing-in-memory (PIM) techniques have gained much attention from computer architecture researchers, and significant research effort has been invested in exploring and developing such techniques.
Kazi Asifuzzaman, Narasinga Rao Miniskar, Aaron R. Young, Frank Liu, Jeffrey S. Vetter   +4 more
doaj   +1 more source

A Comparative Study of Reservoir Computing for Temporal Signal Processing [PDF]

, 2014
Reservoir computing (RC) is a novel approach to time series prediction using recurrent neural networks. In RC, an input signal perturbs the intrinsic dynamics of a medium called a reservoir.
Banda, Peter, Goudarzi, Alireza, Lakin, Matthew R., Stefanovic, Darko, Teuscher, Christof   +4 more
core   +2 more sources

Overcoming the Memory Bottleneck in Auxiliary Field Quantum Monte Carlo Simulations with Interpolative Separable Density Fitting

, 2018
We investigate the use of interpolative separable density fitting (ISDF) as a means to reduce the memory bottleneck in auxiliary field quantum Monte Carlo (AFQMC) simulations of real materials in Gaussian basis sets.
Malone, Fionn D, Morales, Miguel A., Zhang, Shuai   +2 more
core   +2 more sources

Self-rectifying resistive memory in passive crossbar arrays

Nature Communications, 2021
Memory-centric computing refers to computing designs where the memory, rather than the processor is central in the architecture. Here, the authors demonstrate a self-rectifying resistive memory cell that exhibits impressive endurance, and low power ...
Kanghyeok Jeon, Jeeson Kim, Jin Joo Ryu, Seung-Jong Yoo, Choongseok Song, Min Kyu Yang, Doo Seok Jeong, Gun Hwan Kim   +7 more
doaj   +1 more source

Matrix Mapping on Crossbar Memory Arrays with Resistive Interconnects and Its Use in In-Memory Compression of Biosignals

Micromachines, 2019
Recent advances in nanoscale resistive memory devices offer promising opportunities for in-memory computing with their capability of simultaneous information storage and processing.
Yoon Kyeung Lee, Jeong Woo Jeon, Eui-Sang Park, Chanyoung Yoo, Woohyun Kim, Manick Ha, Cheol Seong Hwang   +6 more
doaj   +1 more source

Short-term memory capacity analysis of Lu3Fe4Co0.5Si0.5O12-based spin cluster glass towards reservoir computing

Scientific Reports, 2023
Reservoir computing is a brain heuristic computing paradigm that can complete training at a high speed. The learning performance of a reservoir computing system relies on its nonlinearity and short-term memory ability.
Zhiqiang Liao, Hiroyasu Yamahara, Kenyu Terao, Kaijie Ma, Munetoshi Seki, Hitoshi Tabata   +5 more
doaj   +1 more source

DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car

, 2018
We present DeepPicar, a low-cost deep neural network based autonomous car platform. DeepPicar is a small scale replication of a real self-driving car called DAVE-2 by NVIDIA.
Bechtel, Michael G., Kim, Minje, McEllhiney, Elise, Yun, Heechul   +3 more
core   +1 more source