Enabling Efficient Intermittent Computing on Brand New Microcontrollers via Tracking Programmable Voltage Thresholds

Modern off-the-shelf low-power microcontrollers (MCUs) are optimized to meet the computational requirements of data- and compute-intensive embedded artificial intelligence applications. However, they are not intended for batteryless operation; therefore, they lack fast and low-power non-volatile memory in their architecture. This memory is essential for backup and recovery operations during intermittent execution due to frequent power failures. Connecting an external non-volatile memory to these MCUs exposes a significant time and energy overhead, making them inefficient and even useless for batteryless applications. In this paper, we answer how to enable the adaptation of the brand-new off-the-shelf low-power AI MCUs to the intermittent computing paradigm. To this end, we present a new configurable low-power circuit that brings energy awareness, which is exploited by a novel backup policy that reduces the number of backups significantly. Our evaluation shows that the proposed backup t...

Modern off-the-shelf low-power microcontrollers (MCUs) are optimized to meet the computational requirements of data- and computeintensive embedded artificial intelligence applications. However, they are not intended for batteryless operation; therefore, they lack fast and low-power non-volatile memory in their architecture. This memory is essential for backup and recovery operations during intermittent execution due to frequent power failures. Connecting an external non-volatile memory to these MCUs exposes a significant time and energy overhead, making them inefficient and even useless for batteryless applications. In this paper, we answer how to enable the adaptation of the brand-new off-the-shelf low-power AI MCUs to the intermittent computing paradigm. To this end, we present a new configurable low-power circuit that brings energy awareness, which is exploited by a novel backup policy that reduces the number of backups significantly. Our evaluation shows that the proposed backup technique reduces the execution latency by 40%, eliminating unnecessary backups and hence decreasing the intermittent computing systems’ throughput significantly.