Intermittent computing (ImC) refers to the scenario where periods of program execution are separated by reboots. This computing paradigm is common in some IoT devices. ImC systems are generally powered by energy-harvesting devices: they start executing a program when the accumulated energy reaches a threshold and stop when the energy buffer is exhausted. Since ImC does not depend on a fixed supply of power, it can be used in a wide range of scenarios/devices such as medical implants, wearables, IoT sensors, extraterrestrial systems and so on.
Although attractive, ImC also brings challenges such as avoiding data-loss and data inconsistency, and striking the right balance between performance, energy and quality of the result.
We survey 80+ techniques and systems for ImC. We organize the works on key metrics to expose their similarities and differences. We review circuit and architecture-level techniques, software-level
techniques and those proposed for performing checkpointing and ensuring correctness. Examples of techniques we review are DVFS, approximate computing, hybrid memory (e.g., SRAM-NVM), loop-tiling, compiler support, debugging techniques, avoiding starvation, reducing the amount of data to be backup, etc.
Paper available here, accepted in JSA 2020.
Although attractive, ImC also brings challenges such as avoiding data-loss and data inconsistency, and striking the right balance between performance, energy and quality of the result.
We survey 80+ techniques and systems for ImC. We organize the works on key metrics to expose their similarities and differences. We review circuit and architecture-level techniques, software-level
techniques and those proposed for performing checkpointing and ensuring correctness. Examples of techniques we review are DVFS, approximate computing, hybrid memory (e.g., SRAM-NVM), loop-tiling, compiler support, debugging techniques, avoiding starvation, reducing the amount of data to be backup, etc.
Paper available here, accepted in JSA 2020.