Heterogeneous processor composition: metrics and methods

Heterogeneous processors intended for mobile devices are composed of a number of different CPU cores that enable the processor to optimize performance under strict power limits that vary over time. Design space exploration techniques can be used to discover a candidate set of potential cores that could be implemented on a heterogeneous processor. However, candidate sets contain far more cores than can feasibly be implemented. Heterogeneous processor composition therefore requires solutions to the selection problem and the evaluation problem. Cores must be selected from the candidate set, and these cores must be shown to be quantitatively superior to alternative selections. The qualitative criterion for a selection of cores is diversity. A diverse set of heterogeneous cores allows a processor to execute tasks with varying dynamic behaviors at a range of power and performance levels that are appropriate for conditions during runtime. This thesis presents a detailed description of the selection and evaluation problems, and establishes a theoretical framework for reasoning about the runtime behavior of power-limited, heterogeneous processors. The evaluation problem is specifically concerned with evaluating the collective attributes of selections of cores rather than evaluating the features of individual cores. A suite of metrics is defined to address the evaluation problem. The metrics quantify considerations that could otherwise only be evaluated subjectively. The selection problem is addressed with an iterative, diversity-preserving algorithm that emphasizes the flexibility available to programs at runtime. The algorithm includes facilities for guiding the selection process with information from an expert, when available. Three variations on the selection algorithm are defined. A thorough analysis of the proposed selection algorithm is presented using data from a large-scale simulation involving 33 benchmarks and 3000 core types. The three variations of the algorithm are compared to each other and to current, state-of-the-art selection techniques. The analysis serves as both an evaluation of the proposed algorithm as well as a case study of the metrics

Navigating the Landscape for Real-time Localisation and Mapping for Robotics, Virtual and Augmented Reality

Visual understanding of 3D environments in real-time, at low power, is a huge computational challenge. Often referred to as SLAM (Simultaneous Localisation and Mapping), it is central to applications spanning domestic and industrial robotics, autonomous vehicles, virtual and augmented reality. This paper describes the results of a major research effort to assemble the algorithms, architectures, tools, and systems software needed to enable delivery of SLAM, by supporting applications specialists in selecting and configuring the appropriate algorithm and the appropriate hardware, and compilation pathway, to meet their performance, accuracy, and energy consumption goals. The major contributions we present are (1) tools and methodology for systematic quantitative evaluation of SLAM algorithms, (2) automated, machine-learning-guided exploration of the algorithmic and implementation design space with respect to multiple objectives, (3) end-to-end simulation tools to enable optimisation of heterogeneous, accelerated architectures for the specific algorithmic requirements of the various SLAM algorithmic approaches, and (4) tools for delivering, where appropriate, accelerated, adaptive SLAM solutions in a managed, JIT-compiled, adaptive runtime context.Comment: Proceedings of the IEEE 201