DSPBooster: Offloading Unmodified Mobile Applications to DSPs for Power-performance Optimal Execution

Mobile cloud computing offloads intensive code to remote servers to improve execution performance and battery lifetime. Unfortunately, it is prone to data breaches and dependent on network connectivity. In light of these issues, we explore the potential of an under-utilized local computing resource: Digital Signal Processors (DSPs). Programmable DSPs are widely equipped in mobile devices and can conduct mathematical operations at high speed and low power. However, existing mobile applications rarely offload computation to DSPs due to two reasons. Firstly, conventional DSP development requires high proficiency in low-level programming languages. Secondly, DSP application deployment involves many complex steps such as kernel memory allocation and remote procedure calls. In this paper, we introduce DSPBooster, a framework to facilitate application offloading to DSPs for power-performance optimal execution. DSPBooster supports unmodified applications implemented in various high-level programming languages. It transparently deploys suitable application functions to DSPs based on runtime measurement and prediction. Implementing such a system entails many technical challenges thanks to DSPs' unique micro-architecture and inter-processor communication mechanism. In this paper, we provide workable solutions and a thorough system evaluation. We show that DSPBooster can provide up to 11 % performance gain and 3 × power reduction.