Data dependent energy modelling for worst case energy consumption analysis

Safely meeting Worst Case Energy Consumption (WCEC) criteria requires accurate energy modeling of software. We investigate the impact of instruction operand values upon energy consumption in cacheless embedded processors. Existing instruction-level energy models typically use measurements from random input data, providing estimates unsuitable for safe WCEC analysis. We examine probabilistic energy distributions of instructions and propose a model for composing instruction sequences using distributions, enabling WCEC analysis on program basic blocks. The worst case is predicted with statistical analysis. Further, we verify that the energy of embedded benchmarks can be characterised as a distribution, and compare our proposed technique with other methods of estimating energy consumption

Automated Exploration of the ASIC Design Space for Minimum Power-Delay-Area Product at the Register Transfer Level

Exploring the integrated circuit design space for minimum power-delay-area (PDA) product can be time-consuming and tedious, especially when the target standard-cell library has hundreds of options. In this dissertation, heuristic algorithms that automate this process have been developed, implemented and validated at the reg- ister transfer level. In some cases, the PDA product was 1.9 times better than the initial baseline solution. The parallel search algorithm exhibited 9x speed up when executed on 10 machines simultaneously. These two new methods also characterize the design space for the given RTL code by generating power-delay-area points in addition to the minimum PDA point in case the designer wishes to select a different solution that is a tradeoff among these metrics. As a final step, these two search algorithms are integrated into a fully automated ASIC design flow

Hyperspectral Unmixing on Multicore DSPs: Trading Off Performance for Energy

Wider coverage of observation missions will increase onboard power restrictions while, at the same time, pose higher demands from the perspective of processing time, thus asking for the exploration of novel high-performance and low-power processing architectures. In this paper, we analyze the acceleration of spectral unmixing, a key technique to process hyperspectral images, on multicore architectures. To meet onboard processing restrictions, we employ a low-power Digital Signal Processor (DSP), comparing processing time and energy consumption with those of a representative set of commodity architectures. We demonstrate that DSPs offer a fair balance between ease of programming, performance, and energy consumption, resulting in a highly appealing platform to meet the restrictions of current missions if onboard processing is required