Today's smart devices have short battery lifetimes, high installation and maintenance costs, and rapid obsolescence - all leading to the explosion of electronic waste in the past two decades. These problems will worsen as the number of connected devices grows to one trillion by 2035. Energy harvesting, battery-free devices offer an alternative. Getting rid of the battery reduces e-waste, promises long lifetimes, and enables deployment in new applications and environments. Unfortunately, developing sophisticated inference-capable applications is still challenging. The lack of platform support for advanced (32-bit) microprocessors and specialized accelerators, which can execute data-intensive machine-learning tasks, has held back batteryless devices.
Protean: ADAPTIVE HARDWARE-ACCELERATED INTERMITTENT COMPUTING / Bakar, A; Goel, R; de Winkel, J; Huang, J; Ahmed, S; Islam, B; Pawelczak, P; Yildirim, Ks; Hester, J. - In: GETMOBILE. - ISSN 2375-0529. - 27:1(2023), pp. 5-10. [10.1145/3599184.3599186]
Protean: ADAPTIVE HARDWARE-ACCELERATED INTERMITTENT COMPUTING
Yildirim, KS;
2023-01-01
Abstract
Today's smart devices have short battery lifetimes, high installation and maintenance costs, and rapid obsolescence - all leading to the explosion of electronic waste in the past two decades. These problems will worsen as the number of connected devices grows to one trillion by 2035. Energy harvesting, battery-free devices offer an alternative. Getting rid of the battery reduces e-waste, promises long lifetimes, and enables deployment in new applications and environments. Unfortunately, developing sophisticated inference-capable applications is still challenging. The lack of platform support for advanced (32-bit) microprocessors and specialized accelerators, which can execute data-intensive machine-learning tasks, has held back batteryless devices.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
