More Like this

1. Low-Energy and CPA-Resistant Adiabatic CMOS/MTJ Logic for IoT Devices

   https://doi.org/10.1109/ISVLSI51109.2021.00064  

   Kahleifeh, Zachary; Thapliyal, Himanshu (July 2021, 2021 IEEE Computer Society Annual Symposium on VLSI (ISVLSI))

   The tremendous growth in the number of Internet of Things (IoT) devices has increased focus on the energy efficiency and security of an IoT device. In this paper, we will present a design level, non-volatile adiabatic architecture for low-energy and Correlation Power Analysis (CPA) resistant IoT devices. IoT devices constructed with CMOS integrated circuits suffer from high dynamic energy and leakage power. To solve this, we look at both adiabatic logic and STT-MTJs (Spin Transfer Torque Magnetic Tunnel Junctions) to reduce both dynamic energy and leakage power. Furthermore, CMOS integrated circuits suffer from side-channel leakage making them insecure against power analysis attacks. We again look to adiabatic logic to design secure circuits with uniform power consumption, thus, defending against power analysis attacks. We have developed a hybrid adiabatic-MTJ architecture using two-phase adiabatic logic. We show that hybrid adiabatic-MTJ circuits are both low energy and secure when compared with CMOS circuits. As a case study, we have constructed one round of PRESENT and have shown energy savings of 64.29% at a frequency of 25 MHz. Furthermore, we have performed a correlation power analysis attack on our proposed design and determined that the key was kept hidden. 

   more » « less
2. Novel mmWave/5G RFID Architectures for Next Generation of IoT Devices and VR Applications

   https://doi.org/10.1109/RFID-TA64374.2024.10965148  

   Callis, Theodore W; Joshi, Marvin; Tentzeris, Manos M (December 2024, IEEE)

   As the recent advances in mmWave/5G technologies become increasingly available, the potential for wireless Internet of Things (IoT) devices for precise localization, and high datarate communication systems become highly feasible for AR/VR applications. At the same time, it is essential that the new generation of mmWave systems for IoT applications are lowcost, energy-efficient, easily-scalable, reliable and compact. This work presents some of the recent efforts in mmWave/5G RFID systems that combine novel designs for overcoming the challenges in long-range interrogation, wide-angular coverage, and power consumption. This work describes the state of the evolving field of mmIDs through paradigm examples in literature that enhance mmID localization and orientation, making mmID-based AR/VR applications more feasible. Index Terms—millimeter 

   more » « less
3. Energy-Efficient Data Transfer Optimization via Decision-Tree Based Uncertainty Reduction

   https://doi.org/10.1109/ICCCN54977.2022.9868866  

   Jamil, Hasibul; Rodolph, Lavone; Goldverg, Jacob; Kosar, Tevfik (July 2022, 2022 International Conference on Computer Communications and Networks (ICCCN))

   The increase and rapid growth of data produced by scientific instruments, the Internet of Things (IoT), and social media is causing data transfer performance and resource consumption to garner much attention in the research community. The network infrastructure and end systems that enable this extensive data movement use a substantial amount of electricity, measured in terawatt-hours per year. Managing energy consumption within the core networking infrastructure is an active research area, but there is a limited amount of work on reducing power consumption at the end systems during active data transfers. This paper presents a novel two-phase dynamic throughput and energy optimization model that utilizes an offline decision-search-tree based clustering technique to encapsulate and categorize historical data transfer log information and an online search optimization algorithm to find the best application and kernel layer parameter combination to maximize the achieved data transfer throughput while minimizing the energy consumption. Our model also incorporates an ensemble method to reduce aleatoric uncertainty in finding optimal application and kernel layer parameters during the offline analysis phase. The experimental evaluation results show that our decision-tree based model outperforms the state-of-the-art solutions in this area by achieving 117% higher throughput on average and also consuming 19% less energy at the end systems during active data transfers. 

   more » « less
4. AMPERE: A Generic Energy Estimation Approach for On-Device Training

   https://doi.org/10.1145/3764944.3764951  

   Zhang, Jiaru; Wang, Zesong; Wang, Hao; Song, Tao; Su, Huai-an; Chen, Rui; Hua, Yang; Zhou, Xiangwei; Ma, Ruhui; Pan, Miao; et al (August 2025, ACM SIGMETRICS Performance Evaluation Review)

   Battery-powered mobile devices (e.g., smartphones, AR/VR glasses, and various IoT devices) are increasingly being used for AI training due to their growing computational power and easy access to valuable, diverse, and real-time data. On-device training is highly energy-intensive, making accurate energy consumption estimation crucial for effective job scheduling and sustainable AI. However, the heterogeneity of devices and the complexity of models challenge the accuracy and generalizability of existing methods. This paper proposes AMPERE, a generic approach for energy consumption estimation in deep neural network (DNN) training. First, we examine the layer-wise energy additivity property of DNNs and strategically partition the entire model into layers for fine-grained energy consumption profiling. Then, we fit Gaussian Process (GP) models to learn from layer-wise energy consumption measurements and estimate a DNN's overall energy consumption based on its layer-wise energy additivity property. We conduct extensive experiments with various types of models across different real-world platforms. The results demonstrate that AMPERE has effectively reduced the Mean Absolute Percentage Error (MAPE) by up to 30%. Moreover, AMPERE is applied in guiding energy-aware pruning, successfully reducing energy consumption by 50%, thereby further demonstrating its generality and potential. 

   more » « less
5. Energy per Operation Optimization for Energy-Harvesting Wearable IoT Devices

   https://doi.org/10.3390/s20030764  

   Park, Jaehyun; Bhat, Ganapati; NK, Anish; Geyik, Cemil S.; Ogras, Umit Y.; Lee, Hyung Gyu (February 2020, Sensors)

   Wearable internet of things (IoT) devices can enable a variety of biomedical applications, such as gesture recognition, health monitoring, and human activity tracking. Size and weight constraints limit the battery capacity, which leads to frequent charging requirements and user dissatisfaction. Minimizing the energy consumption not only alleviates this problem, but also paves the way for self-powered devices that operate on harvested energy. This paper considers an energy-optimal gesture recognition application that runs on energy-harvesting devices. We first formulate an optimization problem for maximizing the number of recognized gestures when energy budget and accuracy constraints are given. Next, we derive an analytical energy model from the power consumption measurements using a wearable IoT device prototype. Then, we prove that maximizing the number of recognized gestures is equivalent to minimizing the duration of gesture recognition. Finally, we utilize this result to construct an optimization technique that maximizes the number of gestures recognized under the energy budget constraints while satisfying the recognition accuracy requirements. Our extensive evaluations demonstrate that the proposed analytical model is valid for wearable IoT applications, and the optimization approach increases the number of recognized gestures by up to 2.4× compared to a manual optimization. 

   more » « less