Results 1 to 10 of about 1,025,950 (315)

Benchmarking In-Memory Computing Architectures

IEEE Open Journal of the Solid-State Circuits Society, 2022
In-memory computing (IMC) architectures have emerged as a compelling platform to implement energy-efficient machine learning (ML) systems. However, today, the energy efficiency gains provided by IMC designs seem to be leveling off and it is not clear ...
Naresh R. Shanbhag, Saion K. Roy
doaj   +3 more sources

First demonstration of in-memory computing crossbar using multi-level Cell FeFET. [PDF]

Nat Commun, 2023
Advancements in AI led to the emergence of in-memory-computing architectures as a promising solution for the associated computing and memory challenges.
Soliman T, Chatterjee S, Laleni N, Müller F, Kirchner T, Wehn N, Kämpfe T, Chauhan YS, Amrouch H.   +8 more
europepmc   +2 more sources

Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators. [PDF]

Nat Commun, 2023
Analog in-memory computing—a promising approach for energy-efficient acceleration of deep learning workloads—computes matrix-vector multiplications but only approximately, due to nonidealities that often are non-deterministic or nonlinear.
Rasch MJ, Mackin C, Le Gallo M, Chen A, Fasoli A, Odermatt F, Li N, Nandakumar SR, Narayanan P, Tsai H, Burr GW, Sebastian A, Narayanan V.   +12 more
europepmc   +3 more sources

Emerging 2D Ferroelectric Devices for In-Sensor and In-Memory Computing. [PDF]

Adv Mater
The quantity of sensor nodes within current computing systems is rapidly increasing in tandem with the sensing data. The presence of a bottleneck in data transmission between the sensors, computing, and memory units obstructs the system's efficiency and ...
Chen C, Zhou Y, Tong L, Pang Y, Xu J.
europepmc   +2 more sources

Toward memristive in-memory computing: principles and applications. [PDF]

Front Optoelectron, 2022
With the rapid growth of computer science and big data, the traditional von Neumann architecture suffers the aggravating data communication costs due to the separated structure of the processing units and memories. Memristive in-memory computing paradigm
Bao H, Zhou H, Li J, Pei H, Tian J, Yang L, Ren S, Tong S, Li Y, He Y, Chen J, Cai Y, Wu H, Liu Q, Wan Q, Miao X.   +15 more
europepmc   +2 more sources

Recent Advances in In-Memory Computing: Exploring Memristor and Memtransistor Arrays with 2D Materials. [PDF]

Nanomicro Lett
State-of-the-art research on two-dimensional material-based memristive arrays is comprehensively reviewed. Critical steps in achieving in-memory computing are identified and highlighted, covering material selection, device performance analysis, and array
Zhou H, Li S, Ang KW, Zhang YW.
europepmc   +2 more sources

Achieving software-equivalent accuracy for hyperdimensional computing with ferroelectric-based in-memory computing. [PDF]

Sci Rep, 2022
Hyperdimensional computing (HDC) is a brain-inspired computational framework that relies on long hypervectors (HVs) for learning. In HDC, computational operations consist of simple manipulations of hypervectors and can be incredibly memory-intensive.
Kazemi A, Müller F, Sharifi MM, Errahmouni H, Gerlach G, Kämpfe T, Imani M, Hu XS, Niemier M.   +8 more
europepmc   +2 more sources

An in-memory computing architecture based on two-dimensional semiconductors for multiply-accumulate operations. [PDF]

Nat Commun, 2021
In-memory computing may enable multiply-accumulate (MAC) operations, which are the primary calculations used in artificial intelligence (AI). Performing MAC operations with high capacity in a small area with high energy efficiency remains a challenge. In
Wang Y, Tang H, Xie Y, Chen X, Ma S, Sun Z, Sun Q, Chen L, Zhu H, Wan J, Xu Z, Zhang DW, Zhou P, Bao W.   +13 more
europepmc   +2 more sources

90% yield production of polymer nano-memristor for in-memory computing. [PDF]

Nat Commun, 2021
Polymer memristors with light weight and mechanical flexibility are preeminent candidates for low-power edge computing paradigms. However, the structural inhomogeneity of most polymers usually leads to random resistive switching characteristics, which ...
Zhang B, Chen W, Zeng J, Fan F, Gu J, Chen X, Yan L, Xie G, Liu S, Yan Q, Baik SJ, Zhang ZG, Chen W, Hou J, El-Khouly ME, Zhang Z, Liu G, Chen Y.   +17 more
europepmc   +2 more sources

Memory Optimization Method for Parallel Computing Framework Based on Distributed Dataset [PDF]

Jisuanji gongcheng, 2023
With the rapid development of scientific computing and artificial intelligence technology, parallel computing in distributed environment has become an important method for solving large-scale theoretical computing and data processing problems.
XIA Libin, LIU Xiaoyu, JIANG Xiaowei, SUN Gongxing
doaj   +1 more source