Profile driven dataflow optimisation of mean shift visual tracking

Abstract

Abstract-Profile guided optimisation is a common technique used by compilers and runtime systems to shorten execution runtimes and to optimise locality aware scheduling and memory access on heterogeneous hardware platforms. Some profiling tools trace the execution of low level code, whilst others are designed for abstract models of computation to provide rich domainspecific context in profiling reports. We have implemented mean shift, a computer vision tracking algorithm, in the RVC-CAL dataflow language and use both dynamic runtime and static dataflow profiling mechanisms to identify and eliminate bottlenecks in our naive initial version. We use these profiling reports to tune the CPU scheduler reducing runtime by 88%, and to optimise our dataflow implementation that reduces runtime by a further 43% -an overall runtime reduction of 93%. We also assess the portability of our mean shift optimisations by trading off CPU runtime against resource utilisation on FPGAs. Applying all dataflow optimisations reduces FPGA design space significantly, requiring fewer slice LUTs and less block memory.

Profile Guided Dataflow Transformation for FPGAs and CPUs

“…This paper is an elaboration of earlier work [4], which took the same case study expressed with the CAL dataflow language [12], and optimised it for shorter CPU runtime. Here, we significantly extend that work by first defining a set of dataflow transformations, and obtain profiles using methodical dataflow simulation and FPGA simulation.…”