Work

MTL-Split: Multi-Task Learning for Edge Devices using Split Computing

Year: 2024

Type: conference-paper

Abstract: Split Computing (SC), where a Deep Neural Network (DNN) is intelligently split with a part of it deployed on an edge device and the rest on a remote server is emerging as a promising approach. It allows the power of DNNs to be leveraged for latency-sensitive applications that do not allow the entire DNN to be deployed remotely, while not having sufficient computation bandwidth available locally. In many such embedded systems scenarios, such as those in the automotive domain, computational resource constraints also necessitate Multi-Task Learning (MTL), where the same DNN is used for multiple inference tasks instead of having dedicated DNNs for each task, which would need more computing bandwidth. However, how to partition such a multi-tasking DNN to be deployed within a SC framework has not been sufficiently studied. This paper studies this problem, and MTL-Split, our novel proposed architecture, shows encouraging results on both synthetic and real-world data. The source code is available at https://github.com/intelligolabs/MTL-Split.

Source: ACM/IEEE Design Automation Conference (DAC)

DOI: 10.1145/3649329.3655686

Authors: Luigi Capogrosso, Enrico Fraccaroli, Samarjit Chakraborty, Franco Fummi, Marco Cristani

Institutions: University of Verona, University of North Carolina at Chapel Hill

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Funders: European Commission, HORIZON EUROPE Framework Programme, HORIZON EUROPE Marie Sklodowska-Curie Actions