Spendthrift: Machine learning based resource and frequency scaling for ambient energy harvesting nonvolatile processors

Ma, Kaisheng; Li, Xueqing; Srinivasa, Srivatsa; Liu, Yongpan; Sampson, John J.; Xie, Yuan; Narayanan, Vijaykrishnan

doi:10.1109/aspdac.2017.7858402

Cited by 21 publications

(11 citation statements)

References 21 publications

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“…Most energy-efficient techniques are leveraging network intelligence to achieve a more efficient result. With a lot of cognitive network-based EE applications proposed in literature, artificial intelligence is expected to play a crucial role in EE for future networks [74], including for efficient adaptive resource allocation, discontinuous reception [75], channel learning for power management [76,77], traffic offloading for energy efficiency in small cells [78], node device authentication for security [79], and intermittent energy management for energy-harvested applications [80,81]. We present a list of prediction-based techniques for WCNs in Table 3.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Energy Efficient Design Techniques in Next-Generation Wireless Communication Networks: Emerging Trends and Future Directions

Ogbebor

Imoize

Atayero

2020

Wireless Communications and Mobile Computing

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The projected rise in wireless communication traffic has necessitated the advancement of energy-efficient (EE) techniques for the design of wireless communication systems, given the high operating costs of conventional wireless cellular networks, and the scarcity of energy resources in low-power applications. The objective of this paper is to examine the paradigm shifts in EE approaches in recent times by reviewing traditional approaches to EE, analyzing recent trends, and identifying future challenges and opportunities. Considering the current energy concerns, nodes in emerging wireless networks range from limited-energy nodes (LENs) to high-energy nodes (HENs) with entirely different constraints in either case. In view of these extremes, this paper examines the principles behind energy-efficient wireless communication network design. We then present a broad taxonomy that tracks the areas of impact of these techniques in the network. We specifically discuss the preponderance of prediction-based energy-efficient techniques and their limits, and then discuss the trends in renewable energy supply systems for future networks. Finally, we recommend more context-specific energy-efficient research efforts and cross-vendor collaborations to push the frontiers of energy efficiency in the design of wireless communication networks.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Energy Efficient Design Techniques in Next-Generation Wireless Communication Networks: Emerging Trends and Future Directions

Ogbebor

Imoize

Atayero

2020

Wireless Communications and Mobile Computing

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“…Furthermore, with on-chip NVM and associated sensing and control, a nonvolatile processor (NVP) could be built to back up the processor states and data, including memory, D flip-flops (DFF), registers, etc., into this NVM during power failures [42] [49][50][51][52][53][54][55][56]. Such on-chip data backup and restore operations reduce the risk of losing computation progress.…”

Section: Energy-efficient Nonvolatile Logic and Memory Circuitsmentioning

confidence: 99%

“…One approach is to dynamically switch between different processing cores which are all embedded on the same chip based on level of harvested power and the store energy [51]. The second approach is to dynamically scale the operating frequency and voltage (DVFS) accordingly [55] [56]. The third approach is to dynamically re-allocate computing and storage resources for the processor which turns out to be a different degree of parallelism [56].…”

Section: Nonvolatile Computing Architecturesmentioning

confidence: 99%

“…The second approach is to dynamically scale the operating frequency and voltage (DVFS) accordingly [55] [56]. The third approach is to dynamically re-allocate computing and storage resources for the processor which turns out to be a different degree of parallelism [56].…”

Section: Nonvolatile Computing Architecturesmentioning

confidence: 99%

“…Meanwhile, for some other energy sources, such as motion and solar energy sources, the harvested power has a certain pattern and could be predicted. Machine learning techniques have been proposed to predict the future energy to assist dynamic system configuration for more forward progress [51] [56].…”

Section: Nonvolatile Computing Architecturesmentioning

confidence: 99%

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Enabling Internet-of-Things with Opportunities Brought by Emerging Devices, Circuits and Architectures

George

et al. 2017

IFIP Advances in Information and Communication Technology

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In recent years, the concept of Internet-of-Things (IoT) has attracted significant interests. Required by the applications, the IoT power optimization has become the key concern, which relies on innovations from all levels of device, circuits, and architectures. Meanwhile, the energy efficiency of existing IoT implementations based on the CMOS technology is fundamentally limited by the device physics and also the circuits and systems built on it. This chapter focuses on a different dimension, exploring how emerging beyond-CMOS devices, such as tunnel field effect transistor (TFET) and negative capacitance FET (NCFET), and the circuits and architectures built upon them, could extend the low-power design space to enable IoT applications with beyond-CMOS features.

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Nonvolatile Processor Architecture Exploration for Energy-Harvesting Application Scenarios

Ma¹,

Li²,

Narayanan³

et al. 2019

Embedded, Cyber-Physical, and IoT Systems

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Spendthrift: Machine learning based resource and frequency scaling for ambient energy harvesting nonvolatile processors

Cited by 21 publications

References 21 publications

Energy Efficient Design Techniques in Next-Generation Wireless Communication Networks: Emerging Trends and Future Directions

Energy Efficient Design Techniques in Next-Generation Wireless Communication Networks: Emerging Trends and Future Directions

Enabling Internet-of-Things with Opportunities Brought by Emerging Devices, Circuits and Architectures

Nonvolatile Processor Architecture Exploration for Energy-Harvesting Application Scenarios

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