Hugo Reymond, Jean-Luc Béchennec , Mikaël Briday, Sébastien Faucou, Isabelle Puaut and Erven Rohou will be presenting SCHEMATIC at CGO’24 in Edinburgh.
SCHEMATIC is a checkpoint placement and memory allocation technique that guarantees forward progress in intermittent programs, and reduces the energy consumed by the program by 50% on average.
A preprint of the paper is available on HAL: https://hal.science/hal-04345348
A short teaser of SCHEMATIC, introducing the compile-time technique, is available on youtube :
We will be presenting SCHEMATIC at CGO 2024, join us at Edinburgh !
Battery-free devices enable sensing in hard-to-access locations, opening up new opportunities in various fields such as healthcare, space, or civil engineering.
Such devices harvest ambient energy and store it in a capacitor. Due to the unpredictable nature of the harvested energy, a power failure can occur at any time,
resulting in a loss of all non-persistent information (e.g processor registers, data stored in volatile memory).
Checkpointing volatile data in non-volatile memory allows the system to recover after a power failure, but raises two issues:
(i) spatial and temporal placement of checkpoints;
(ii) memory allocation of variables between volatile and non-volatile memory,
with the overall objective of using energy as efficiently as possible.
While many techniques rely on the developer to address these issues, we present Schematic, a compiler technique that automates checkpoint placement and
memory allocation to minimize the overall energy consumption. Schematic ensures that programs will eventually terminate (forward progress property).
Moreover, checkpoint placement and memory allocation adapt to the size of the energy buffer and the capacity of volatile memory.
Schematic takes advantage of volatile memory (VM) to reduce the energy consumed, by automatically placing the most used variables in VM.
We tested Schematic for different experimental settings (size of volatile memory and capacitor) and results show an average energy reduction of 51% compared
to related techniques.
This work has received a French government support granted to the Labex Cominlabs excellence laboratory and managed by the National Research Agency
in the “Investing for the Future” program under reference ANR-10-LABX-07-01.