This talk focuses on the challenge of software self-configurability. I introduce a methodology based on statistical machine learning for systematic automated optimisation of algorithmic and implementation parameters to achieve end-to-end quality-of-result objectives as well as energy and performance goals. I show the effectiveness of our approach on next generation real-time 3D scene understanding applications where configurability is especially important. I briefly introduce SLAMBench, a publicly-available benchmarking framework which represents a starting point for quantitative, comparable and validatable experimental research to investigate trade-offs in performance, accuracy and energy consumption of 3D scene understanding systems. I then examine how it can be mapped to power constrained embedded systems. The goal of this work is to take the human out of the loop and to provide self-optimisation of complex software and hardware pipelines.

Dr. Luigi Nardi is a post-doctoral research associate at Imperial College London in the Software Performance Optimisation group. Luigi's primary role is to work in the co-design of high-performance low-power computer vision systems where performance, power and accuracy are part of the same optimisation space. Luigi earned his Ph.D. in computer science from UPMC, France, creating a new performance domain-specific language in the context of automatic code generation for applied mathematics. He has almost 10 years of experience in parallel computing and more than 6 years of experience developing GPU enabled codes using CUDA and OpenCL from desktop to embedded. Prior to his current position, Luigi was a permanent researcher at Murex S.A.S., France, working on the acceleration of production-level computational finance codes for pricing evaluation and risk management on clusters of GPUs.