Parallel in-memory wireless computing

Wang, Cong; Ruan, Gong-Jie; Yang, Zai-Zheng; Yangdong, Xing-Jian; Li, Yixiang; Wu, Liang; Ge, Yingmeng; Zhao, Yichen; Pan, Chen; Wei, Wei; Wang, Li-Bo; Cheng, Bin; Zhang, Zaichen; Zhang, Chuan; Liang, Shi-Jun; Miao, Feng

doi:10.1038/s41928-023-00965-5

Cited by 22 publications

(7 citation statements)

References 43 publications

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“…For instance, the emerging inmemory computing devices and in-sensor computing devices should be developed in the use of neuromorphic computing systems and numerical computations. [157][158][159] As neural networks continue to scale up, it is becoming increasingly necessary, from a hardware implementation perspective, to develop device structures that are more suitable for integration to facilitate larger-scale production. Simple and reliable device structures, as well as mature fabrication and integration processes get to be emergent.…”

Section: Discussionmentioning

confidence: 99%

Recent progress on ambipolar 2D semiconductors in emergent reconfigurable electronics and optoelectronics

Zhao 赵,

Sun 孙,

Sheng 盛

et al. 2023

Chinese Phys. B

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In these days, the increasing massive data is being produced and demanded to be processed with the rapid growth of information technology. It is difficult to rely solely on the shrinking of semiconductor devices and scale-up of the integrated circuits (ICs) again in the foreseeable future. Exploring new materials, new-principle semiconductor devices and new computing architectures is becoming an urgent topic in this field. Ambipolar two-dimensional (2D) semiconductors, possessing excellent electrostatic field controllability and flexibly modulated major charge carriers, offer a possibility to construct reconfigurable devices and enable the ICs with new functions, showing great potential in computing capacity, energy efficiency, time delay and cost. This review focuses on the recent significant advancements on reconfigurable electronic and optoelectronic devices of ambipolar 2D semiconductors, and demonstrates their potential approach towards ICs, like reconfigurable circuits and neuromorphic chips. It is expected to help readers to understand the device design principle of ambipolar 2D semiconductors, and push forwards exploring more new-principle devices and new-architecture computing circuits, and even their product applications.

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Section: Discussionmentioning

confidence: 99%

Recent progress on ambipolar 2D semiconductors in emergent reconfigurable electronics and optoelectronics

Zhao 赵,

Sun 孙,

Sheng 盛

et al. 2023

Chinese Phys. B

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“…Traditionally expensive operations such as vector-matrix multiplication can be completed in parallel, resulting in O(1) time, by leveraging physical laws; namely Kirchhoff's Law for summation and Ohm's Law for multiplication. Recent advances are now demonstrating the capabilities of these devices, including wireless communications [10], fully analog ML accelerators [11] and greatly expanded storage density [12].…”

Section: Background On Memristor Technologymentioning

confidence: 99%

“…The use of complex-valued DNNs in combination with various feature "augmentation strategies" has shown promise in developing protocol agnostic and variation-resilient fingerprinting techniques [19,22,23]. , memristor technology is fully capable of processing complex-valued data and takes advantage of these specialized architectures [10]. Difficulties still exist in the elimination of confounding factors.…”

Section: Rf Fingerprintingmentioning

confidence: 99%

“…Memristive crossbars show great promise in non-ML RF applications. By connecting memristive crossbars directly with analog wireless media, Wang et al were able to "store, transfer and modulate time-varying analog signals and parallelly process data without digital-to-analog converters (DACs) and analog-to-digital converters (ADCs)" [10]. This integration yielded an increase in power efficiency of over 100-fold.…”

Section: Real World Applicationsmentioning

confidence: 99%

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Survey of Security Issues in Memristor-Based Machine Learning Accelerators for RF Analysis

Lillis,

Hoffing,

Burleson

2024

Chips

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We explore security aspects of a new computing paradigm that combines novel memristors and traditional Complimentary Metal Oxide Semiconductor (CMOS) to construct a highly efficient analog and/or digital fabric that is especially well-suited to Machine Learning (ML) inference processors for Radio Frequency (RF) signals. Analog and/or hybrid hardware designed for such application areas follows different constraints from that of traditional CMOS. This paradigm shift allows for enhanced capabilities but also introduces novel attack surfaces. Memristors have different properties than traditional CMOS which can potentially be exploited by attackers. In addition, the mixed signal approximate computing model has different vulnerabilities than traditional digital implementations. However both the memristor and the ML computation can be leveraged to create security mechanisms and countermeasures ranging from lightweight cryptography, identifiers (e.g., Physically Unclonable Functions (PUFs), fingerprints, and watermarks), entropy sources, hardware obfuscation and leakage/attack detection methods. Three different threat models are proposed: (1) Supply Chain, (2) Physical Attacks, and (3) Remote Attacks. For each threat model, potential vulnerabilities and defenses are identified. This survey reviews a variety of recent work from the hardware and ML security literature and proposes open problems for both attack and defense. The survey emphasizes the growing area of RF signal analysis and identification in terms of commercial space, as well as military applications and threat models. We differ from other recent surveys that target ML, in general, neglecting RF applications.

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“…In this approach, there is no need to move the data between the computing unit and memory, which reduces energy consumption. Additionally, the memristor-based VMM serves as an accelerator for DNN inference due to its inherent parallelism (Wang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Memristor-Based In-Memory Computing Architecture for Scientific Computing

Li²,

Ling³

et al. 2022

Memristor Computing Systems

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The advancement in the field of machine learning is inextricably linked with the concurrent progress in domain-specific hardware accelerators such as GPUs and TPUs. However, the rapidly growing computational demands necessitated by larger models and increased data have become a primary bottleneck in further advancing machine learning, especially in mobile and edge devices. Currently, the neuromorphic computing paradigm based on memristors presents a promising solution. In this study, we introduce a memristorbased MobileNetV3 neural network computing paradigm and provide an end-to-end framework for validation. The results demonstrate that this computing paradigm achieves over 90% accuracy on the CIFAR-10 dataset while saving inference time and reducing energy consumption. With the successful development and verification of MobileNetV3, the potential for realizing more memristor-based neural networks using this computing paradigm and open-source framework has significantly increased. This progress sets a groundbreaking pathway for future deployment initiatives.

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Parallel in-memory wireless computing

Cited by 22 publications

References 43 publications

Recent progress on ambipolar 2D semiconductors in emergent reconfigurable electronics and optoelectronics

Recent progress on ambipolar 2D semiconductors in emergent reconfigurable electronics and optoelectronics

Survey of Security Issues in Memristor-Based Machine Learning Accelerators for RF Analysis

Memristor-Based In-Memory Computing Architecture for Scientific Computing

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