Batteryless sensors avoid battery replacement at the cost of slowing down or stopping their operations when there is not sufficient energy to harvest in the environment. While this strategy can work for some applications, event-based applications still remain a challenge as events arrive sporadically and energy availability is uncertain. One solution is to only turn On a sensor right before an event is happening to both detect the event and save as much energy as possible. Therefore, the system has to correctly predict events while managing limited resource availability. In this demo, we present Ember, an energy management system based on deep reinforcement learning to duty cycle event-driven sensors in low-energy conditions. We show how our system learns environmental patterns over time and makes decisions to maximize the event detection rate for batteryless energy-harvesting sensor nodes subject to low energy availability. Furthermore, we show a novel self-supervised data collection algorithm that helps Ember in discovering new environmental patterns over time. For more details, we refer readers to the full paper of Ember [2].

Energy management can extend the lifetime of batteryless, energy-harvesting systems by judiciously utilizing the energy available. Duty cycling of such systems is especially challenging for event detection, as events arrive sporadically and energy availability is uncertain. If the node sleeps too much, it may miss important events; if it depletes energy too quickly, it will stop operating in low energy conditions and miss events. Thus, accurate event prediction is important in making this tradeoff. We propose Ember, an energy management system based on deep reinforcement learning to duty cycle event-driven sensors in low energy conditions. We train a policy using historical real-world data traces of motion, temperature, humidity, pressure, and light events. The resulting policy can learn to capture up to 95% of the events without depleting the node. Without historical data for training when deploying a node at a new location, we propose a self-supervised mechanism to collect ground-truth data while learning from the data at the same time. Ember learns to capture the majority of events within a week without any historical data and matches the performance of the policies trained with historical data in a few weeks. We deployed 40 nodes running Ember for indoor sensing and demonstrate that the learned policies generalize to real-world settings as well as outperform state-of-the-art techniques.

Many modern smart building applications are supported by wireless sensors to sense physical parameters, given the flexibility they offer and the reduced cost of deployment. However, most wireless sensors are powered by batteries today, and large deployments are inhibited by the requirement of periodic battery replacement. Energy harvesting sensors provide an attractive alternative, but they need to provide adequate quality of service to applications given the uncertainty of energy availability. We propose ACES, which uses reinforcement learning to maximize sensing quality of energy harvesting sensors for periodic and event-driven indoor sensing with available energy. Our custom-built sensor platform uses a supercapacitor to store energy and Bluetooth Low Energy to relay sensors data. Using simulations and real deployments, we use the data collected to continually adapt the sensing of each node to changing environmental patterns and transfer learning to reduce the training time in real deployments. In our 60-node deployment lasting 2 weeks, nodes stop operations only 0.1% of the time, and collection of data is comparable with current battery-powered nodes. We show that ACES reduces the node duty-cycle period by an average of 33% compared to three prior reinforcement learning techniques while continuously learning environmental changes over time.

We propose two optimization techniques to minimize memory usage and computation while meeting system timing constraints for real-time classification in wearable systems. Our method derives a hierarchical classifier structure for Support Vector Machine (SVM) in order to reduce the amount of computations, based on the probability distribution of output classes occurrences. Also, we propose a memory optimization technique based on SVM parameters, which results in storing fewer support vectors and as a result requiring less memory. To demonstrate the efficiency of our proposed techniques, we performed an activity recognition experiment and were able to save up to 35% and 56% in memory storage when classifying 14 and 6 different activities, respectively. In addition, we demonstrated that there is a trade-off between accuracy of classification and memory savings, which can be controlled based on application requirements.

Supercomputers, nowadays, aggregate a large number of nodes featuring the same nominal HW components (e.g., processors and GPGPUS). In real-life machines, the chips populating each node are subject to a wide range of variability sources, related to performance and temperature operating points (i.e., ACPI p-states) as well as process variations and die binning. Eurora is a fully operational supercomputer prototype that topped July 2013 Green500 and it represents a unique 'living lab' for next-generation ultra-green supercomputers. In this paper we evaluate and quantify the impact of variability on Eurora's energy-performance tradeoffs under a wide range of workloads intensity. Our experiments demonstrate that variability comes from hardware component mismatches as well as from the interplay between run-time energy management and workload variations. Thus, variability has a significant impact on energy efficiency even at the moderate scale of the Eurora machine, thereby substantiating the critical importance of variability management in future green supercomputers.

Supercomputers, nowadays, aggregate a large number of nodes sharing the same nominal HW components (eg. processors and GPGPUS). In real-life machines, the chips populating each node are subject to a wide range of variability sources, related to performance and temperature operating points (i.e. ACPI p-states) as well as process variations and die binning. Eurora is a fully operational supercomputer prototype that topped July 2013 Green500 and it represents a unique 'living lab' for next-generation ultra-green supercomputers. In this paper we evaluate and quantify the impact of variability on Eurora's energy-performance tradeoffs under a wide range of workload intensity.