Sparse matrix multiplication in a record-low power self-rectifying memristor array for scientific computing

Li, Jiancong; Ren, Shengguang; Li, Yi; Ling, Yang; Yu, Yingjie; Ni, Run; Zhou, Houji; Bao, Han; He, Yuhui; Chen, Jia; Han, Je-Chin; Miao, Xiang

doi:10.1126/sciadv.adf7474

Cited by 16 publications

(2 citation statements)

References 55 publications

(96 reference statements)

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“…Moreover, the energy efficiency of AI models remains a critical area for improvement. Future research should focus on these challenges, aiming to ensure the responsible and effective use of AI and DL technologies for a more sustainable future [124,125].…”

Section: Energy Efficiency In Ai and DL Modelsmentioning

confidence: 99%

Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health

Fan,

Yan,

Wen

2023

Sustainability

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Artificial intelligence (AI) and deep learning (DL) have shown tremendous potential in driving sustainability across various sectors. This paper reviews recent advancements in AI and DL and explores their applications in achieving sustainable development goals (SDGs), renewable energy, environmental health, and smart building energy management. AI has the potential to contribute to 134 of the 169 targets across all SDGs, but the rapid development of these technologies necessitates comprehensive regulatory oversight to ensure transparency, safety, and ethical standards. In the renewable energy sector, AI and DL have been effectively utilized in optimizing energy management, fault detection, and power grid stability. They have also demonstrated promise in enhancing waste management and predictive analysis in photovoltaic power plants. In the field of environmental health, the integration of AI and DL has facilitated the analysis of complex spatial data, improving exposure modeling and disease prediction. However, challenges such as the explainability and transparency of AI and DL models, the scalability and high dimensionality of data, the integration with next-generation wireless networks, and ethics and privacy concerns need to be addressed. Future research should focus on enhancing the explainability and transparency of AI and DL models, developing scalable algorithms for processing large datasets, exploring the integration of AI with next-generation wireless networks, and addressing ethical and privacy considerations. Additionally, improving the energy efficiency of AI and DL models is crucial to ensure the sustainable use of these technologies. By addressing these challenges and fostering responsible and innovative use, AI and DL can significantly contribute to a more sustainable future.

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Section: Energy Efficiency In Ai and DL Modelsmentioning

confidence: 99%

Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health

Fan,

Yan,

Wen

2023

Sustainability

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“…The emergence of computing-in-memory (CIM) technology can largely reduce power consumption and delay costs during data transmission. For certain computations, such as matrix and vector multiplication (MVM), the calculation process can be performed on the memory device [23,24]. Therefore, there is no need to transmit data to the computing unit to complete calculation tasks, saving a significant amount of energy.…”

Section: Flash Memorymentioning

confidence: 99%

Flash-Based Computing-in-Memory Architecture to Implement High-Precision Sparse Coding

Qi,

Feng,

Wang

et al. 2023

Micromachines

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To address the concerns with power consumption and processing efficiency in big-size data processing, sparse coding in computing-in-memory (CIM) architectures is gaining much more attention. Here, a novel Flash-based CIM architecture is proposed to implement large-scale sparse coding, wherein various matrix weight training algorithms are verified. Then, with further optimizations of mapping methods and initialization conditions, the variation-sensitive training (VST) algorithm is designed to enhance the processing efficiency and accuracy of the applications of image reconstructions. Based on the comprehensive characterizations observed when considering the impacts of array variations, the experiment demonstrated that the trained dictionary could successfully reconstruct the images in a 55 nm flash memory array based on the proposed architecture, irrespective of current variations. The results indicate the feasibility of using Flash-based CIM architectures to implement high-precision sparse coding in a wide range of applications.

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

Ren,

Dong,

Yang

et al. 2023

Advanced Materials

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Costly data movement in terms of time and energy in traditional von Neumann systems is exacerbated by emerging information technologies related to artificial intelligence (AI). In‐memory computing (IMC) architecture aims to address this problem. Although the IMC hardware prototype represented by a memristor has developed rapidly and performs well, the sneak path issue is a critical and unavoidable challenge prevalent in large‐scale and high‐density crossbar arrays, particularly in three‐dimensional (3D) integration. As a perfect solution to the sneak‐path issue, self‐rectifying memristor (SRM) has been proposed for 3D integration because of its superior integration density. To date, SRMs have performed well in terms of power consumption (∼aJ) and scalability (> 102 Mbit). Moreover, SRM‐configured 3D integration is considered an ideal hardware platform for 3D IMC. This review focuses on the progress in SRMs and their applications in 3D memory, IMC, neuromorphic computing, and hardware security. The advantages, disadvantages, and optimization strategies of SRMs in diverse application scenarios are illustrated. Challenges posed by physical mechanisms, fabrication processes, and peripheral circuits, as well as potential solutions at the device and system levels, are also discussed.This article is protected by copyright. All rights reserved

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Sparse matrix multiplication in a record-low power self-rectifying memristor array for scientific computing

Cited by 16 publications

References 55 publications

Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health

Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health

Flash-Based Computing-in-Memory Architecture to Implement High-Precision Sparse Coding

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

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