The Case for Exploiting Underutilized Resources in Heterogeneous Mobile Architectures

Heterogeneous architectures are ubiquitous in mobile platforms, with mobile SoCs typically integrating multiple processors along with accelerators such as GPUs (for data-parallel kernels) and DSPs (for signal processing kernels). This strict partitioning of application execution on heterogeneous compute resources often results in underutilization of resources such as DSPs. We present a case study executing a mix of popular data-parallel workloads such as convolutional neural networks (CNNs), computer vision filters and graphics rendering kernels on mobile devices, and show that both performance and energy consumption of mobile platforms can be improved by synergistically deploying these underutilized compute resources. Our experiments on a mobile Snapdragon 835 platform under both single and multiple application scenarios executing the aforementioned workloads demonstrates average performance and energy improvements of 15-46% and 18-80%, respectively, by synergistically deploying all available compute resources, especially the underutilized DSP.