As robotic manipulators adopt increasingly higher degrees of freedom (DoFs) to handle complex tasks, the corresponding growth in neural network (NN) size leads to substantial memory and energy demands, making deployment on low-level controllers increasingly impractical. To overcome this challenge, we propose STAR, a novel framework that enables accurate and energy-efficient high-DoF manipulation under strict memory constraints. STAR introduces a spherical task-space approximation strategy to mathematically formulate the manipulator’s reachable space, followed by a memory-aware training algorithm that adaptively divides this space into smaller, manageable regions, with each partition assigned a lightweight NN optimized to satisfy memory capacity while preserving high precision. Specifically, STAR employs deep reinforcement learning (DRL) to learn absolute pose-to-joint mappings, allowing each task to be completed with a single NN load, eliminating the need for large networks or frequent NN switching. Experiments demonstrate that STAR achieves up to 8.09× faster execution and 10.93× lower energy consumption, while reducing memory usage by up to 128× compared to state-of-the-art approaches, all without compromising control accuracy.

The increasing maturity of energy harvesting technologies has brought intermittent systems to the forefront as viable solutions for a range of applications. One critical area is environmental monitoring, where timely and accurate reporting of environmental conditions is essential. Existing approaches on systems powered by unstable ambient energy focus on maintaining the freshness of collected data but fall short when applied to neural network workloads, as they often neglect model accuracy. Prior studies have explored deploying neural networks on intermittent systems using branchy architectures, which prioritize energy-accuracy tradeoffs by terminating inference early. However, these approaches fail to address time constraints, often resulting in system failures due to processing expired results. This article introduces iTRAIN, a novel timing-aware framework for deploying neural network models on intermittent systems. iTRAIN holistically accounts for energy availability, timing constraints, and model accuracy. Unlike previous studies that depend on branchy architectures, iTRAIN leverages a composable neural network framework to broaden the solution space, enabling diverse energy-time-accuracy tradeoffs. This is achieved through runtime selection among various layer implementations, such as pruning and quantization, guided by a reinforcement learning algorithm. Experimental results demonstrate that iTRAIN outperforms state-of-the-art approaches, achieving a 65% improvement in delivered model accuracy with minimal memory and runtime overhead. iTRAIN sets a foundation for enabling complex applications on intermittent systems.

Environmental monitoring applications use energy harvesting to cover wide-range deployment, where devices are powered by ambient energy and operate intermittently when energy is sufficient. In such an intermittent networked system (NIS), a sink node is used to forward the environmental data collected by sensors to a central controller to reflect the physical environment status. Nevertheless, existing data forwarding algorithms for NISs cannot fulfill modern application requirements, where multiple types of data with different timeliness requirements (i.e., multipriorities) are desired to report real-time environmental data for monitoring critical situations. Without considering the multipriorities, we show in this article that it introduces a new problem: unevenness of data freshness. We then propose the sink node-based evenness-aware update forwarding (iSAFE) algorithm to provide evenness among different priorities of data sources in NISs. iSAFE consists of three important components: 1) a theoretical analysis to derive the optimal data forwarding interval between two adjacent status updates from the sensor; 2) an evenness-aware forwarding algorithm to adaptively adjust the forwarding interval based on the runtime status; and 3) a fresh-aware energy preservation algorithm to maintain the freshness of collected data. The experimental results show that iSAFE can achieve up to 682% evenness (94.47% close to the ideal) and 53.3% data freshness compared to the state of the art while being energy-efficient and scalable, suitable for modern applications.

NAND flash memory has become the dominant storage media choice in a vast majority of application scenarios. Compared to mechanical hard disks, flash offers better access performance, energy efficiency, and shock resistance. However, the unique hardware peculiarities of this technology require dedicated facilities to manage the flash space and data. The implementation of flash management facilities has alternatively been realized either at the device or host computer level. Managing flash on the device side eases integration/compatibility and increases performance in certain scenarios. However, the limited computing resources inherent to devices and the lack of higher-level file system/application information make these solutions suboptimal in many situations. Managing flash on the host allows leveraging its abundant resources, and host-side knowledge such as data access patterns can be exploited to optimize flash management, at the cost of increased host-side complexity. The pros and cons of each approach also led to the appearance of hybrid, cross-layer solutions, enabling the collaboration of different layers of the storage stack. Recently, the pressure on modern storage systems requires that an increasing amount of flash management responsibilities is offloaded to the host, and the development of application-specific cross-layer solutions: In that context, it is crucial to review these developments. In this article, we make a comprehensive survey of the host-side management technologies of flash memory, application-/system-level flash-friendly designs, and emergent applications based on flash memory.

A networked intermittent system (NIS) is often deployed in the field for environmental monitoring, where sink nodes are responsible for relaying the data captured by sensors to a central system. To evaluate the quality of the captured monitoring data, Age of Information (AoI) is adopted to quantify the freshness of the data received by the central server. As the sink nodes are powered by ambient energy sources (e.g., solar and wind), the energy-efficient design of the sink nodes is crucial in order to improve the system-wide AoI. This work proposes the energy-efficient sink node design to save energy and extend system uptime. We devise an AoI-aware data forwarding algorithm based on the branch-and-bound (B&B) paradigm for deriving the optimal solution offline. In addition, an AoI-aware data forwarding algorithm is developed to approximate the optimal solution during runtime. The experimental results show that our solution can greatly improve the average data freshness for 148% against existing well-known strategies and achieves 91 % performance of the optimal solution. Compared with the state-of-the-art algorithm, our energy-efficient design can deliver better$A^{3}oI$results by up to 9.6%.

Intermittent systems become popular to be considered as the solutions of various application domains, thanks to the maturation of energy harvesting technology. Environmental monitoring is such an example and it is a time-sensitive application domain. In order to report the perceived environmental status in a timely manner, methods have been proposed to consider the freshness of the collected information on such systems with unstable power sources. Nevertheless, these methods cannot be applied to neural network workloads since these methods do not consider the delivered model accuracy. On the other hand, while there have been studies for deploying neural network applications on intermittent systems, they depend on branchy network architectures, each branch representing an energy-accuracy tradeoff, and do not take into account a time constraint, which tends to cause system failures because of the frequent generation of expired data. In this work, the first timing-aware framework TRAIN is proposed to deploy the neural network models on the intermittent systems by considering energy, time constraint, and delivered model accuracy. Compared with the prior studies that depend on branchy network architectures, TRAIN offers a broadened solution space representing various energy/time/accuracy tradeoffs. It is achieved by allowing to choose among different implementations of each model layer during the model inference at runtime, and the smart choices are made by the proposed reinforcement learning algorithm. Our results demonstrate TRAIN outperforms the prior study by 65%, regarding the delivered model accuracy. We believe that TRAIN paves the way for building complex applications on intermittent systems.

Energy harvesting devices powered by ambient energies, instead of batteries, have been drawn lots of attention due to their advantages of energy saving, easy deployment without relying on stable power sources, and smaller sizes, facilitating promising applications, such as environmental and health monitoring. These devices perform the computations intermittently, where the code executions are halted and resumed depending on the availability of the harvested energy. On such devices, the capacitors are present and served as the energy buffers for preserving the program states when sudden power outages occur. Nevertheless, the capacitors have relatively shorter lifetimes, compared with the rest of hardware components on the devices, and larger capacitors, which are desired by the systems requiring complex computations, hamper the achievement of device miniaturization, e.g., for medical implants or smart dust. In this article, we propose a new intermittent checkpointing strategy,iCheck, to tackle the issues raised for the program-state retaining when the capacitors are not functioning correctly (or when the capacitor-less devices are adopted). The proposediCheckis designed to perform the checkpointing-based program-state preserving progressively with being aware of the power-failure characteristics of the harvested energy source to maximize the progress forwarding and to ensure data consistency while encountering incomplete checkpoints caused by sudden power losses. The proposed design is evaluated with a series of experiments with encouraging results.