  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.
