The Challenges and Emerging Technologies for Low-Power Artificial Intelligence IoT Systems

Ye, Le; Wang, Zhixuan; Liu, Ying; Chen, Peiyu; Li, Heyi; Zhang, Hao; Wu, Meng; He, Wei; Shen, Linxiao; Zhang, Yihan; Tan, Zhichao; Wang, Yangyuan; Huang, Ru

doi:10.1109/tcsi.2021.3095622

Cited by 47 publications

(11 citation statements)

References 92 publications

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“…IoT devices also need to share data securely over the Internet in order to prevent hackers from taking advantage of it, in order to meet the intended privacy aims. And so, in order to prevent data leaks, IoT devices ought to be required to share data using the finest encryption methods, such as AES, RSA, and ECC [15,16].…”

Section: Privacymentioning

confidence: 99%

Blockchain Integration with AIoT Data Security and Privacy for Sustainability

Arora,

Makani

2024

AIS

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As a primary goal, AIoT (Artificial Intelligence of Things) is the fusion of AI (artificial intelligence) methods with IoT (Internet of Things) infrastructure, which is deployed there to enhance the overall system performance of AIoT. Artificial Intelligence of Things can be used to make Internet of Things operations more efficient, which will enhance data analysis and human-machine interactions. The system's general usefulness is further increased by applying artificial intelligence techniques to convert Internet of Things data into relevant information for improved decision-making processes. The Artificial Intelligence of Things frameworks have a wide range of applications, including eCommerce, logistics operations and control, smart homes, smart farms, intelligent transportation systems, industrial automation and control, eCommerce, secure as well as safe healthcare monitoring, and many more. AIoT frameworks, however, are susceptible to a variety of information security-related assaults, which could result in problems with data security and privacy. Serious repercussions, such as unapproved data updates and leaks, are also brought on by these problems. One particular kind of database is the blockchain. It's a digital record of all the transactions that's distributed throughout the whole network of systems. Data is stored in the blocks that are linked in a chained manner. Compared to conventional security methods, blockchain technology offers greater security and is impervious to tampering. Therefore, to increase security, blockchain can be used in a variety of AIoT applications. A safe authentication architecture for AIoT has been suggested, modelled after a generalised blockchain. The adversarial model, which handles most potential security threats in this kind of communication environment, is also highlighted. This framework is part of the blockchain-envisioned safe authentication framework for the Internet of Things. The suggested framework's numerous applications are also covered. Additionally, certain problems and difficulties with the suggested framework are emphasised. Finally, we also offer some suggestions for future research that are related to the framework that has been suggested.

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Section: Privacymentioning

confidence: 99%

Blockchain Integration with AIoT Data Security and Privacy for Sustainability

Arora,

Makani

2024

AIS

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“…resource-constrained edge of the internet (Zhou et al, 2019;Ye et al, 2021), where sensors are generating increasingly unmanageable amounts of data.…”

Section: Introductionmentioning

confidence: 99%

Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directions

Nilsson

Schelén²,

Lindgren³

et al. 2023

Front. Neurosci.

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Increasing complexity and data-generation rates in cyber-physical systems and the industrial Internet of things are calling for a corresponding increase in AI capabilities at the resource-constrained edges of the Internet. Meanwhile, the resource requirements of digital computing and deep learning are growing exponentially, in an unsustainable manner. One possible way to bridge this gap is the adoption of resource-efficient brain-inspired “neuromorphic” processing and sensing devices, which use event-driven, asynchronous, dynamic neurosynaptic elements with colocated memory for distributed processing and machine learning. However, since neuromorphic systems are fundamentally different from conventional von Neumann computers and clock-driven sensor systems, several challenges are posed to large-scale adoption and integration of neuromorphic devices into the existing distributed digital–computational infrastructure. Here, we describe the current landscape of neuromorphic computing, focusing on characteristics that pose integration challenges. Based on this analysis, we propose a microservice-based conceptual framework for neuromorphic systems integration, consisting of a neuromorphic-system proxy, which would provide virtualization and communication capabilities required in distributed systems of systems, in combination with a declarative programming approach offering engineering-process abstraction. We also present concepts that could serve as a basis for the realization of this framework, and identify directions for further research required to enable large-scale system integration of neuromorphic devices.

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“…The CIM approach is accomplished either based on the mixed-signal or all-digital signal domains. The mixed-signal approach evolved earlier, as it achieves better energy efficiency [ 3 ]. However, the analog signal nature restricts such CIM to fewer bits precision and needs additional peripheral circuitry [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

FinFET 6T-SRAM All-Digital Compute-in-Memory for Artificial Intelligence Applications: An Overview and Analysis

Gul,

Shams,

Al-Khalili

2023

Micromachines

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Artificial intelligence (AI) has revolutionized present-day life through automation and independent decision-making capabilities. For AI hardware implementations, the 6T-SRAM cell is a suitable candidate due to its performance edge over its counterparts. However, modern AI hardware such as neural networks (NNs) access off-chip data quite often, degrading the overall system performance. Compute-in-memory (CIM) reduces off-chip data access transactions. One CIM approach is based on the mixed-signal domain, but it suffers from limited bit precision and signal margin issues. An alternate emerging approach uses the all-digital signal domain that provides better signal margins and bit precision; however, it will be at the expense of hardware overhead. We have analyzed digital signal domain CIM silicon-verified 6T-SRAM CIM solutions, after classifying them as SRAM-based accelerators, i.e., near-memory computing (NMC), and custom SRAM-based CIM, i.e., in-memory-computing (IMC). We have focused on multiply and accumulate (MAC) as the most frequent operation in convolution neural networks (CNNs) and compared state-of-the-art implementations. Neural networks with low weight precision, i.e., <12b, show lower accuracy but higher power efficiency. An input precision of 8b achieves implementation requirements. The maximum performance reported is 7.49 TOPS at 330 MHz, while custom SRAM-based performance has shown a maximum of 5.6 GOPS at 100 MHz. The second part of this article analyzes the FinFET 6T-SRAM as one of the critical components in determining overall performance of an AI computing system. We have investigated the FinFET 6T-SRAM cell performance and limitations as dictated by the FinFET technology-specific parameters, such as sizing, threshold voltage (Vth), supply voltage (VDD), and process and environmental variations. The HD FinFET 6T-SRAM cell shows 32% lower read access time and 1.09 times better leakage power as compared with the HC cell configuration. The minimum achievable supply voltage is 600 mV without utilization of any read- or write-assist scheme for all cell configurations, while temperature variations show noise margin deviation of up to 22% of the nominal values.

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The Challenges and Emerging Technologies for Low-Power Artificial Intelligence IoT Systems

Cited by 47 publications

References 92 publications

Blockchain Integration with AIoT Data Security and Privacy for Sustainability

Blockchain Integration with AIoT Data Security and Privacy for Sustainability

Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directions

FinFET 6T-SRAM All-Digital Compute-in-Memory for Artificial Intelligence Applications: An Overview and Analysis

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