no code implementations • 2 May 2024 • Haiguang Li, Usama Pervaiz, Joseph Antognini, Michał Matuszak, Lawrence Au, Gilles Roux, Trausti Thormundsso
To address this, developers often face a trade-off between model accuracy and power consumption, employing either computationally intensive models on high-power cores or pared-down models on low-power cores.
no code implementations • 5 Apr 2024 • Haiguang Li, Usama Pervaiz, Michał Matuszak, Robert Kamara, Gilles Roux, Trausti Thormundsson, Joseph Antognini
The DSL builds upon the GC architecture, leveraging a dynamic pathway selection, and adapting model complexity in response to the innate structure of the data.
no code implementations • 15 Mar 2023 • Haiguang Li, Trausti Thormundsson, Ivan Poupyrev, Nicholas Gillian
Mobile and embedded machine learning developers frequently have to compromise between two inferior on-device deployment strategies: sacrifice accuracy and aggressively shrink their models to run on dedicated low-power cores; or sacrifice battery by running larger models on more powerful compute cores such as neural processing units or the main application processor.
