Spintronics for energy-efficient computing: An overview and outlook

Z Guo, J Yin, Y Bai, D Zhu, K Shi, G Wang… - Proceedings of the …, 2021 - ieeexplore.ieee.org
From the discovery of giant magnetoresistance (GMR) to tunnel magnetoresistance (TMR),
their subsequent application in large capacity hard disk drives (HDDs) greatly speeded up …

A review of in-memory computing architectures for machine learning applications

S Bavikadi, PR Sutradhar, KN Khasawneh… - Proceedings of the …, 2020 - dl.acm.org
to meet the extensive computational load presented by the rapidly growing Machine
Learning (ML) and Artificial Intelligence (AI) algorithms such as Deep Neural Networks …

SIMDRAM: A framework for bit-serial SIMD processing using DRAM

N Hajinazar, GF Oliveira, S Gregorio… - Proceedings of the 26th …, 2021 - dl.acm.org
Processing-using-DRAM has been proposed for a limited set of basic operations (ie, logic
operations, addition). However, in order to enable full adoption of processing-using-DRAM …

A survey on machine learning accelerators and evolutionary hardware platforms

S Bavikadi, A Dhavlle, A Ganguly… - IEEE Design & …, 2022 - ieeexplore.ieee.org
Advanced computing systems have long been enablers for breakthroughs in artificial
intelligence (AI) and machine learning (ML) algorithms, either through sheer computational …

MRIMA: An MRAM-based in-memory accelerator

S Angizi, Z He, A Awad, D Fan - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
In this paper, we propose MRIMA, as a novel magnetic RAM (MRAM)-based in-memory
accelerator for nonvolatile, flexible, and efficient in-memory computing. MRIMA transforms …

A multilevel cell STT-MRAM-based computing in-memory accelerator for binary convolutional neural network

Y Pan, P Ouyang, Y Zhao, W Kang… - IEEE Transactions …, 2018 - ieeexplore.ieee.org
Due to additive operation's dominated computation and simplified network in binary
convolutional neural network (BCNN), it is promising for Internet of Things scenarios which …

[PDF][PDF] pluto: In-dram lookup tables to enable massively parallel general-purpose computation

JD Ferreira, G Falcao, J Gómez-Luna… - arXiv preprint arXiv …, 2021 - academia.edu
Data movement between main memory and the processor is a significant contributor to the
execution time and energy consumption of memory-intensive applications. This data …

pluto: Enabling massively parallel computation in dram via lookup tables

JD Ferreira, G Falcao, J Gómez-Luna… - 2022 55th IEEE/ACM …, 2022 - ieeexplore.ieee.org
Data movement between the main memory and the processor is a key contributor to
execution time and energy consumption in memory-intensive applications. This data …

In-memory logic operations and neuromorphic computing in non-volatile random access memory

QF Ou, BS Xiong, L Yu, J Wen, L Wang, Y Tong - Materials, 2020 - mdpi.com
Recent progress in the development of artificial intelligence technologies, aided by deep
learning algorithms, has led to an unprecedented revolution in neuromorphic circuits …

A survey of spintronic architectures for processing-in-memory and neural networks

S Umesh, S Mittal - Journal of Systems Architecture, 2019 - Elsevier
The rising overheads of data-movement and limitations of general-purpose processing
architectures have led to a huge surge in the interest in “processing-in-memory”(PIM) …