Work

Accurate Energy Measurements for Tiny Machine Learning Workloads

Year: 2024

Type: conference-paper

Source: IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)

DOI: 10.1109/metroxraine62247.2024.10797006

Authors: Pasquale Arpaia, Luigi Capobianco, Francesco Caputo, Antonella Cioffi, Antonio Esposito, Francesco Isgrò, Nicola Moccaldi, Danilo Pau, David Siorpaes, Ettore Toscano

Institutions: University of Naples Federico II, STMicroelectronics (Italy)

Language: English

Topic: Low-power high-performance VLSI design

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: closed

OpenAlex ID: W4405755383

FWCI: 3.893

Cites: 14

Cited by: 5

Related to: 10