Results 251 to 260 of about 1,025,950 (315)

Self‐Rectifying Memristors for Three‐Dimensional In‐Memory Computing

Advances in Materials, 2023
Costly data movement in terms of time and energy in traditional von Neumann systems is exacerbated by emerging information technologies related to artificial intelligence. In‐memory computing (IMC) architecture aims to address this problem.
Shengguang Ren, A-Wei Dong, Ling Yang, Yi-Bai Xue, Jiancong Li, Yinjie Yu, Houji Zhou, Wen-Bin Zuo, Yi Li, Wei-Ming Cheng, Xiangshui Miao   +10 more
semanticscholar   +1 more source

Emerging 2D Memory Devices for In‐Memory Computing

Advances in Materials, 2021
It is predicted that the conventional von Neumann computing architecture cannot meet the demands of future data‐intensive computing applications due to the bottleneck between the processing and memory units.
Lei Yin, Ruiqing Cheng, Y. Wen, Chuansheng Liu, Jun He   +4 more
semanticscholar   +1 more source

Scalable In-Memory Computing

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2015
Data-intensive scientific workflows are composed of many tasks that exhibit data precedence constraints leading to communication schemes expressed by means of intermediate files. In such scenarios, the storage layer is often a bottleneck, limiting overall application scalability, due to large volumes of data being generated during runtime at high I/O ...
Uta, A., Sandu, A., Costache, S.V., Kielmann, T.   +3 more
openaire   +2 more sources

XNOR-SRAM: In-Memory Computing SRAM Macro for Binary/Ternary Deep Neural Networks

2018 IEEE Symposium on VLSI Technology, 2018
We present an in-memory computing SRAM macro that computes XNOR-and-accumulate in binary/ternary deep neural networks on the bitline without row-by-row data access. It achieves 33X better energy and 300X better energy-delay product than digital ASIC, and
Zhewei Jiang, Shihui Yin, Mingoo Seok, Jae-sun Seo   +3 more
semanticscholar   +1 more source

Computing In-Memory, Revisited

2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), 2018
The Von Neumann's architecture has been the dominant computing paradigm ever since its inception in the mid-forties. It revolves around the concept of a "stored program" in memory, and a central processing unit that executes the program. As an alternative, Processing-In-Memory (PIM) ideas have been around for at least two decades, however with very ...
Dejan Milojicic, Kirk Bresniker, Gary Campbell, Paolo Faraboschi, John Paul Strachan, Stan Williams   +5 more
openaire   +1 more source

A 64-Tile 2.4-Mb In-Memory-Computing CNN Accelerator Employing Charge-Domain Compute

IEEE Journal of Solid-State Circuits, 2019
Large-scale matrix-vector multiplications, which dominate in deep neural networks (DNNs), are limited by data movement in modern VLSI technologies. This paper addresses data movement via an in-memory-computing accelerator that employs charged-domain ...
Hossein Valavi, P. Ramadge, E. Nestler, N. Verma   +3 more
semanticscholar   +1 more source

A Flexible and Reliable RRAM-Based In-Memory Computing Architecture for Data-Intensive Applications

IEEE Transactions on Emerging Topics in Computing, 2023
This article proposes a practical, flexible, and reliable in-memory computing architecture for resistive-memory-based logic designs. Our design uses a new RRAM-based polymorphic in-memory logic gate implementing all 2-input Boolean logic functions to ...
Nima Eslami, M. H. Moaiyeri
semanticscholar   +1 more source

Multi-Logic Sense Amplifier (MLSA) Design for In-Memory Computing

IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2023
The widely used Von Neumann architecture must move data between memory and the computation unit when performing operations, which causes a performance bottleneck.
Yen-Jen Chang, Shih-Hsiang Chen
semanticscholar   +1 more source

Enabling Energy-Efficient In-Memory Computing With Robust Assist-Based Reconfigurable Sense Amplifier in SRAM Array

IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2023
With the increasing gap between processing speed and memory bandwidth necessity for in/near-memory computing has emerged, to ensure high-performance, energy-efficient computing for data-intensive applications at the edge.
Kavitha Soundrapandiyan, S. Vishvakarma, B. Reniwal   +2 more
semanticscholar   +1 more source

Computing-in-memory with spintronics

2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2018
In-memory computing is a promising approach to alleviating the processor-memory data transfer bottleneck in computing systems. While spintronics has attracted great interest as a non-volatile memory technology, recent work has shown that its unique properties can also enable in-memory computing.
Shubham Jain, Sachin Sapatnekar, Jian-Ping Wang, Kaushik Roy, Anand Raghunathan   +4 more
openaire   +1 more source