Adaptive DNN Surgery for Selfish Inference Acceleration with On-demand
  Edge Resource
Xiang Yang Dezhi Chen Qi Qi Jingyu Wang Haifeng Sun Jianxin Liao Song Guo
Deep Neural Networks (DNNs) have significantly improved the accuracy of
intelligent applications on mobile devices. DNN surgery, which partitions DNN
processing between mobile devices and multi-access edge computing (MEC)
servers, can enable real-time inference despite the computational limitations
of mobile devices. However, DNN surgery faces a critical challenge: determining
the optimal computing resource demand from the server and the corresponding
partition strategy, while considering both inference latency and MEC server
usage costs. This problem is compounded by two factors: (1) the finite
computing capacity of the MEC server, which is shared among multiple devices,
leading to inter-dependent demands, and (2) the shift in modern DNN
architecture from chains to directed acyclic graphs (DAGs), which complicates
potential solutions.
  In this paper, we introduce a novel Decentralized DNN Surgery (DDS)
framework. We formulate the partition strategy as a min-cut and propose a
resource allocation game to adaptively schedule the demands of mobile devices
in an MEC environment. We prove the existence of a Nash Equilibrium (NE), and
develop an iterative algorithm to efficiently reach the NE for each device. Our
extensive experiments demonstrate that DDS can effectively handle varying MEC
scenarios, achieving up to 1.25$\times$ acceleration compared to the
state-of-the-art algorithm.