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Low Power Circuits and Systems for IoT Autonomous Sensors and...
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Low Power Circuits and Systems for IoT Autonomous Sensors and Sensor Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 11946

Special Issue Editor

Dr. Sijun Du

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
Interests: microelectronics; ultra-low power-integrated circuits; Internet of Things (IoT); autonomous sensors; energy harvesting; wireless power/data transfer; power conversion; renewable energy; wearable electronics; biomedical electronics; microrobotics

Special Issue Information

Dear Colleagues,

Along with the recent advances in the Internet of Things (IoT), sensors are playing an important role in connecting the physical and the cyber worlds. Since IoT sensors are ubiquitously implemented, it is very impractical and costly to periodically charge or replace the batteries in these ubiquitous sensors. In order to make these IoT sensors autonomous and self-sustained, there are several feasible approaches, including harvesting energy from the environment, designing low power sensors and sensor interface circuits, and proposing low power data processing and wireless communication algorithms. This Special Issue will focus on emerging technologies in energy harvesting, power management, low power sensors, and sensor networks to make IoT wireless sensors fully self-sustained or to significantly prolong the battery lifetime with circuit-, system-, and algorithm-level designs. We invite authors to contribute original research articles, as well as review articles, which advance the state-of-the-art with innovative solutions for self-sustained or significantly prolonged battery-life IoT wireless sensors.

The topics of this Special Issue will include but are not limited to:

  • Energy harvesting devices, circuits, and systems;
  • Battery-less systems;
  • Autonomous systems;
  • Printed circuits;
  • Remote sensing;
  • Low power sensors;
  • Sensor interface circuits;
  • Low power analog/digital signal processing;
  • Low power wireless communication;
  • Design methodology for low power analog/digital systems.

Dr. Sijun Du
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Low power
  • Energy harvesting
  • Sensors
  • Sensor interface circuits
  • Sensor networks
  • Power management
  • Power transfer
  • Low power communication
  • Internet of Things
  • Autonomous systems

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Published Papers (3 papers)

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Research

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9 pages, 1559 KiB  
Communication
Improved ISR for IEEE 802.11ah Nonlinearity Compensation via Adjustable Constellation Borders
by Li Cho, Cheng-Yu Chen and Chau-Yun Hsu
Electronics 2021, 10(13), 1573; https://doi.org/10.3390/electronics10131573 - 30 Jun 2021
Cited by 2 | Viewed by 1686
Abstract
Iterative subcarrier regularization (ISR) has been recently proposed as a receiver-side remedy for orthogonal frequency division multiplexing (OFDM) nonlinearity. It allows the power amplifier of OFDM transmitters to operate at a lower input back-off for more efficient uplinks. However, the compensation ability cannot [...] Read more.
Iterative subcarrier regularization (ISR) has been recently proposed as a receiver-side remedy for orthogonal frequency division multiplexing (OFDM) nonlinearity. It allows the power amplifier of OFDM transmitters to operate at a lower input back-off for more efficient uplinks. However, the compensation ability cannot align with increasing channel quality, because the standard quadrature amplitude modulation (QAM) used in ISR may eliminate compensation due to erroneous decisions. To solve this issue, an improved version of ISR was proposed to flexibly adjust the constellation borders of QAM and numerically optimize it based on IEEE 802.11ah (hereinafter referred to as 802.11ah) specifications. Simulations show that the proposed scheme not only improves the converged bit error rate of ISR but also accelerates its own convergence, especially in a high channel quality, thereby achieving better power efficiency for Internet of Things clients without additional computational complexity. Full article
(This article belongs to the Special Issue Low Power Circuits and Systems for IoT Autonomous Sensors and Sensor Networks)
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19 pages, 3897 KiB  
Article
Design of Power-Efficient Training Accelerator for Convolution Neural Networks
by JiUn Hong, Saad Arslan, TaeGeon Lee and HyungWon Kim
Electronics 2021, 10(7), 787; https://doi.org/10.3390/electronics10070787 - 26 Mar 2021
Cited by 10 | Viewed by 3570
Abstract
To realize deep learning techniques, a type of deep neural network (DNN) called a convolutional neural networks (CNN) is among the most widely used models aimed at image recognition applications. However, there is growing demand for light-weight and low-power neural network accelerators, not [...] Read more.
To realize deep learning techniques, a type of deep neural network (DNN) called a convolutional neural networks (CNN) is among the most widely used models aimed at image recognition applications. However, there is growing demand for light-weight and low-power neural network accelerators, not only for inference but also for training process. In this paper, we propose a training accelerator that provides low power and compact chip size targeted for mobile and edge computing applications. It accelerates to achieve the real-time processing of both inference and training using concurrent floating-point data paths. The proposed accelerator can be externally controlled and employs resource sharing and an integrated convolution-pooling block to achieve low area and low energy consumption. We implemented the proposed training accelerator in an FPGA (Field Programmable Gate Array) and evaluated its training performance using an MNIST CNN example in comparison with a PC with GPU (Graphics Processing Unit). While both methods achieved a similar training accuracy of 95.1%, the proposed accelerator, when implemented in a silicon chip, reduced the energy consumption by 480 times compared to the counterpart. Additionally, when implemented on an FPGA, an energy reduction of over 4.5 times was achieved compared to the existing FPGA training accelerator for the MNIST dataset. Therefore, the proposed accelerator is more suitable for deployment in mobile/edge nodes compared to the existing software and hardware accelerators. Full article
(This article belongs to the Special Issue Low Power Circuits and Systems for IoT Autonomous Sensors and Sensor Networks)
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Review

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16 pages, 5523 KiB  
Review
Low-Power Ultra-Small Edge AI Accelerators for Image Recognition with Convolution Neural Networks: Analysis and Future Directions
by Weison Lin, Adewale Adetomi and Tughrul Arslan
Electronics 2021, 10(17), 2048; https://doi.org/10.3390/electronics10172048 - 25 Aug 2021
Cited by 15 | Viewed by 5741
Abstract
Edge AI accelerators have been emerging as a solution for near customers’ applications in areas such as unmanned aerial vehicles (UAVs), image recognition sensors, wearable devices, robotics, and remote sensing satellites. These applications require meeting performance targets and resilience constraints due to the [...] Read more.
Edge AI accelerators have been emerging as a solution for near customers’ applications in areas such as unmanned aerial vehicles (UAVs), image recognition sensors, wearable devices, robotics, and remote sensing satellites. These applications require meeting performance targets and resilience constraints due to the limited device area and hostile environments for operation. Numerous research articles have proposed the edge AI accelerator for satisfying the applications, but not all include full specifications. Most of them tend to compare the architecture with other existing CPUs, GPUs, or other reference research, which implies that the performance exposé of the articles are not comprehensive. Thus, this work lists the essential specifications of prior art edge AI accelerators and the CGRA accelerators during the past few years to define and evaluate the low power ultra-small edge AI accelerators. The actual performance, implementation, and productized examples of edge AI accelerators are released in this paper. We introduce the evaluation results showing the edge AI accelerator design trend about key performance metrics to guide designers. Last but not least, we give out the prospect of developing edge AI’s existing and future directions and trends, which will involve other technologies for future challenging constraints. Full article
(This article belongs to the Special Issue Low Power Circuits and Systems for IoT Autonomous Sensors and Sensor Networks)
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