부산대학교 도서관

IoT devices commonly use flash memory for both data and code storage. Flash memory consumes a significant portion of the overall energy of such devices. This is problematic because IoT devices are energy constrained due to their reliance on batteries or energy harvesting. To save energy, we leverage a unique property of flash memory; write operations take unequal amounts of energy depending on if we are flipping a 1 → 0 versus a 0 → 1. We exploit this asymmetry to reduce energy consumption with FLIPBIT, a hardware-software approximation approach that limits costly 0→1 transitions in flash. Instead of performing an exact write, we write an approximated value that avoids any costly 0→1 bit flips. Using FLIPBIT, we reduce the mean energy used by flash by 68% on video streaming applications while maintaining 42 dB PSNR. On machine learning models, we reduce energy by an average of 39% and up to 71% with only a 1% accuracy loss. Additionally, by reducing the number of program-erase cycles, we increase the flash lifetime by 68%.