Related papers: DIAC: Design Exploration of Intermittent-Aware Com…
In low power wireless sensor networks, MAC protocols usually employ periodic sleep/wake schedule to reduce idle listening time. Even though this mechanism is simple and efficient, it results in high end-to-end latency and low throughput. On…
Range anxiety and long recharging times remain critical barriers to electric vehicle adoption. Dynamic Inductive Charging (DIC) offers a compelling solution by enabling wireless power transfer while driving, potentially reducing battery…
Lithium-ion batteries exhibit nonlinear voltage dynamics across varying operating conditions and aging states, making accurate modeling essential for estimation, control, and health monitoring. This work compares two data-driven frameworks…
This paper presents CARTOS, a charging-aware real-time operating system designed to enhance the functionality of intermittently-powered batteryless devices (IPDs) for various Internet of Things (IoT) applications. While IPDs offer…
To improve sustainability, Internet-of-Things (IoT) is increasingly adopting battery-free devices powered by ambient energy scavenged from the environment. The unpredictable availability of ambient energy leads to device intermittency,…
The number of Internet of Things (IoT) devices is increasing exponentially, and it is environmentally and economically unsustainable to power all these devices with batteries. The key alternative is energy harvesting, but battery-less IoT…
We present hardware/software techniques to intelligently regulate supply voltage and clock frequency of intermittently-computing devices. These devices rely on ambient energy harvesting to power their operation and small capacitors as…
We study how to mitigate the effects of energy attacks in the batteryless Internet of Things (IoT). Battery-less IoT devices live and die with ambient energy, as they use energy harvesting to power their operation. They are employed in a…
Task-oriented integrated sensing, communication, and computation (ISCC) is a key technology for achieving low-latency edge inference and enabling efficient implementation of artificial intelligence (AI) in industrial cyber-physical systems…
We present the concept of approximate intermittent computing and demonstrate its application. Intermittent computations stem from the erratic energy patterns caused by energy harvesting: computations unpredictably terminate whenever energy…
The expansion of Artificial Intelligence-generated content service requires diffusion model serving to simultaneously achieve high throughput and low task end-to-end (E2E) latency. However, existing continuous batching methods suffer from…
We study how ambient energy harvesting may be used as an attack vector in the battery-less Internet of Things (IoT). Battery-less IoT devices rely on ambient energy harvesting and are employed in a multitude of applications, including…
Deployment of modern TinyML tasks on small battery-constrained IoT devices requires high computational energy efficiency. Analog In-Memory Computing (IMC) using non-volatile memory (NVM) promises major efficiency improvements in deep neural…
Energy harvesting (EH) IoT devices that operate intermittently without batteries, coupled with advances in deep neural networks (DNNs), have opened up new opportunities for enabling sustainable smart applications. Nevertheless, implementing…
Energy harvesting is a promising solution to power Internet of Things (IoT) devices. Due to the intermittent nature of these energy sources, one cannot guarantee forward progress of program execution. Prior work has advocated for…
The Industrial Internet of Things (IIoT) is an emerging paradigm to make industrial operations more efficient and intelligent by deploying a massive number of wireless devices to industry scenes. However, due to the limited computing…
Many HPC applications can be expressed as mixed-mode computations, in which each node of a computational DAG is itself a parallel computation that can be molded at runtime to allocate different amounts of processing resources. At the same…
A major challenge that is currently faced in the design of applications for the Internet of Things (IoT) concerns with the optimal use of available energy resources given the battery lifetime of the IoT devices. The challenge is derived…
The use of deep learning (DL) on Internet of Things (IoT) and mobile devices offers numerous advantages over cloud-based processing. However, such devices face substantial energy constraints to prolong battery-life, or may even operate…
In this paper, we present a low-power anomaly detection integrated circuit (ADIC) based on a one-class classifier (OCC) neural network. The ADIC achieves low-power operation through a combination of (a) careful choice of algorithm for…