LO-SC: Local-Only Split Computing for Accurate Deep Learning on Edge Devices

Split Computing (SC) enables deploying a Deep Neural Network (DNN) on edge devices with limited resources by splitting the workload between the edge device and a remote server. However, relying on a server can be expensive, requires a reliable network, and introduces unpredictable latency. Existing solutions for on-device DNNs deployment often sacrifice accuracy for efficiency. In this paper, we study how to use the concepts from SC to split a DNN for executing on the same device without compromising accuracy. In other words, we propose Local-Only Split Computing (LO-SC), a new approach to split a DNN for execution entirely on the edge device while maintaining high accuracy and predictable latency. We formalize LO-SC as a MixedInteger Linear Problem (MILP) problem and solve it using a multi-constrained ordered knapsack algorithm. The proposed method achieves promising results on both synthetic and realworld data, offering a viable alternative for accurately deploying DNNs on resource-constrained edge devices. The source code is available at https://github.com/intelligolabs/LO-SC.