| Abstract |
Embedded systems, e.g. in computer vision applications, are expected to provide significant amounts of computing power to process large data volumes. Many of these systems, such as used in medical diagnosis, are mobile devices and face significant challenges to provide sufficient performance while operating on a constrained energy budget.
Modern embedded MPSoC platforms use heterogeneous CPU and GPU cores providing a large number of optimization parameters. This allows to find useful trade-offs between energy consumption and performance for a given application. In this paper, we describe how the complex data processing required for PAMONO, a novel type of biosensor for the detection of biological viruses, can efficiently be implemented on a state-of-the-art heterogeneous MPSoC platform. An additional optimization dimension explored is the achieved quality of service. Reducing the virus detection accuracy enables additional optimizations not achievable by modifying hardware or software parameters alone.
Instead of relying on often inaccurate simulation models, our design space exploration employs a hardware-in-the-loop approach to evaluate the performance and energy consumption on the embedded target platform. Trade-offs between performance, energy and accuracy are controlled by a genetic algorithm running on a PC control system which deploys the evaluation tasks to a number of connected embedded boards. Using our optimization approach, we are able to achieve frame rates meeting the requirements without losing accuracy. Further, our approach is able to reduce the energy consumption by 93% with a still reasonable detection quality.
|
