Results 71 to 80 of about 931,340 (223)

Device and Circuit Architectures for In‐Memory Computing

Advanced Intelligent Systems, 2020
With the rise in artificial intelligence (AI), computing systems are facing new challenges related to the large amount of data and the increasing burden of communication between the memory and the processing unit.
Daniele Ielmini, Giacomo Pedretti
doaj   +1 more source

A Multi-Memristive Unit-Cell Array With Diagonal Interconnects for In-Memory Computing [PDF]

, 2021
Riduan Khaddam-Aljameh, Michele Martemucci, Benedikt Kersting, Manuel Le Gallo, Robert L. Bruce, M. BrightSky, Abu Sebastian   +6 more
openalex   +1 more source

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Small Science
Separate memory and processing units are utilized in conventional von Neumann computational architectures. However, regarding the energy and the time, it is costly to shuffle data between the memory and the processing entity, and for data‐intensive ...
Shao-Xiang Go, Kian-Guan Lim, Tae-Hoon Lee, Desmond K. Loke   +3 more
doaj   +1 more source

Domain-Specific STT-MRAM-Based In-Memory Computing: A Survey

IEEE Access
In recent years, the rapid growth of big data and the increasing demand for high-performance computing have fueled the development of novel computing architectures.
Alaba Yusuf, Tosiron Adegbija, Dhruv Gajaria   +2 more
doaj   +1 more source

Non-volatile in-memory computing [PDF]

, 2019
The analysis of big-data at exa-scale (1018 bytes or flops) has called for an urgent need to re-examine the existing hardware platform that can support intensive data-oriented computing. A big-data-driven application requires huge bandwidth and yet able to ensure low-power density.
openaire   +2 more sources

Energy-efficient analog-domain aggregator circuit for RRAM-based neural network accelerators

Frontiers in Electronics
Recently, there has been notable progress in the advancement of RRAM-based Compute-In-Memory (CIM) architectures, showing promise in accelerating neural networks with remarkable energy efficiency and parallelism.
Khaled Humood, Yihan Pan, Shiwei Wang, Alexander Serb, Themis Prodromakis   +4 more
doaj   +1 more source

Temporal correlation detection using computational phase-change memory

Nature Communications, 2017
New computing paradigms, such as in-memory computing, are expected to overcome the limitations of conventional computing approaches. Sebastian et al. report a large-scale demonstration of computational phase change memory (PCM) by performing high-level ...
Abu Sebastian, Tomas Tuma, Nikolaos Papandreou, Manuel Le Gallo, Lukas Kull, Thomas Parnell, Evangelos Eleftheriou   +6 more
doaj   +1 more source

A review on selective in-memory computing processors: Potential alternative to AI-driven applications

Results in Engineering
In-memory computing (IMC) is a paradigm-shifting approach to data processing that eliminates the sluggishness of transferring data between memory and processing units. By integrating computation directly within the memory, IMC accelerates performance for
Mohith V, Sakthivel R
doaj   +1 more source

Kernel approximation using analogue in-memory computing

Nature Machine Intelligence
Kernel functions are vital ingredients of several machine learning algorithms, but often incur significant memory and computational costs. We introduce an approach to kernel approximation in machine learning algorithms suitable for mixed-signal Analog In-Memory Computing (AIMC) architectures.
Julian Büchel, Giacomo Camposampiero, Athanasios Vasilopoulos, Corey Lammie, Manuel Le Gallo, Abbas Rahimi, Abu Sebastian   +6 more
openaire   +2 more sources

STT-RAM-Based Hierarchical in-Memory Computing

IEEE Transactions on Parallel and Distributed Systems
In-memory computing promises to overcome the von Neumann bottleneck in computer systems by performing computations directly within the memory. Previous research has suggested using Spin-Transfer Torque RAM (STT-RAM) for in-memory computing due to its non-volatility, low leakage power, high density, endurance, and commercial viability.
Dhruv Gajaria, Kevin Antony Gomez, Tosiron Adegbija   +2 more
openaire   +2 more sources