Journal: Journal of Low Power Electronics and Applications

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Publisher

MDPI

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ISSN

2079-9268

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2020 - 20254
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Publications 1 - 4 of 4
  • Idleness-aware dynamic power mode selection on the i.Mx 7ULP iot edge processor
    Item type: Journal Article
    Di Mauro, Alfio; Fatemi, Hamed; Pineda de Gyvez, José; et al. (2020)
    Journal of Low Power Electronics and Applications
    Power management is a crucial concern in micro-controller platforms for the Internet of Things (IoT) edge. Many applications present a variable and difficult to predict workload profile, usually driven by external inputs. The dynamic tuning of power consumption to the application requirements is indeed a viable approach to save energy. In this paper, we propose the implementation of a power management strategy for a novel low-cost low-power heterogeneous dual-core SoC for IoT edge fabricated in 28 nm FD-SOI technology. Ss with more complex power management policies implemented on high-end application processors, we propose a power management strategy where the power mode is dynamically selected to ensure user-specified target idleness. We demonstrate that the dynamic power mode selection introduced by our power manager allows achieving more than 43% power consumption reduction with respect to static worst-case power mode selection, without any significant penalty in the performance of a running application.
  • Batteryless Sensor Devices for Underground Infrastructure-A Long-Term Experiment on Urban Water Pipes
    Item type: Journal Article
    Boebel, Manuel; Frei, Fabian; Blumensaat, Frank; et al. (2023)
    Journal of Low Power Electronics and Applications
    Drinking water is becoming increasingly scarce as the world's population grows and climate change continues. However, there is great potential to improve drinking water pipelines, as 30% of fresh water is lost between the supplier and consumer. While systematic process monitoring could play a crucial role in the early detection and repair of leaks, current practice requires manual inspection, which is both time-consuming and costly. This project envisages maintenance-free measurements at numerous locations within the underground infrastructure, a goal that is to be achieved through the use of a harvesting device mounted on the water pipe. This device extracts energy from the temperature difference between the water pipe and the soil using a TEG (thermoelectric generator), takes sensor measurements, processes the data and transmits it wirelessly via LoRaWAN. We built 16 harvesting devices, installed them in four locations and continuously evaluated their performance throughout the project. In this paper, we focus on two devices of a particular type. The data for a full year show that enough energy was available on 94% of the days, on average, to take measurements and transmit data. This study demonstrates that it is possible to power highly constrained sensing devices with energy harvesting in underground environments.
  • Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSM
    Item type: Journal Article
    Giardino, Michael; Schwyn, Daniel; Ferri, Bonnie; et al. (2022)
    Journal of Low Power Electronics and Applications
    With the computational systems of even embedded devices becoming ever more powerful, there is a need for more effective and pro-active methods of dynamic power management. The work presented in this paper demonstrates the effectiveness of a reinforcement-learning based dynamic power manager placed in a software framework. This combination of Q-learning for determining policy and the software abstractions provide many of the benefits of co-design, namely, good performance, responsiveness and application guidance, with the flexibility of easily changing policies or platforms. The Q-learning based Quality of Service Manager (2QoSM) is implemented on an autonomous robot built on a complex, powerful embedded single-board computer (SBC) and a high-resolution path-planning algorithm. We find that the 2QoSM reduces power consumption up to 42% compared to the Linux on-demand governor and 10.2% over a state-of-the-art situation aware governor. Moreover, the performance as measured by path error is improved by up to 6.1%, all while saving power.
  • Optimizing BFloat16 Deployment of Tiny Transformers on Ultra-Low Power Extreme Edge SoCs
    Item type: Journal Article
    Dequino, Alberto; Bompani, Luca; Benini, Luca; et al. (2025)
    Journal of Low Power Electronics and Applications
    Transformers have emerged as the central backbone architecture for modern generative AI. However, most ML applications targeting low-power, low-cost SoCs (TinyML apps) do not employ Transformers as these models are thought to be challenging to quantize and deploy on small devices. This work proposes a methodology to reduce Transformer dimensions with an extensive pruning search. We exploit the intrinsic redundancy of these models to fit them on resource-constrained devices with a well-controlled accuracy tradeoff. We then propose an optimized library to deploy the reduced models using BFLoat16 with no accuracy loss on Commercial Off-The-Shelf (COTS) RISC-V multi-core micro-controllers, enabling the execution of these models at the extreme edge, without the need for complex and accuracy-critical quantization schemes. Our solution achieves up to 220x speedup with respect to a na & iuml;ve C port of the Multi-Head Self Attention PyTorch kernel: we reduced MobileBert and TinyViT memory footprint up to similar to 94% and similar to 57%, respectively, and we deployed a tinyLLAMA SLM on microcontroller, achieving a throughput of 1219 tokens/s with an average power of just 57 mW.
Publications 1 - 4 of 4