Synthesis of application specific processor architectures for ultra-low energy consumption

In this paper we suggest that further energy savings can be achieved by a new approach to synthesis of embedded processor cores, where the architecture is tailored to the algorithms that the core executes. In the context of embedded processor synthesis, both single-core and many-core, the types of algorithms and demands on the execution efficiency are usually known at the chip design time. This knowledge can be utilised at the design stage to synthesise architectures optimised for energy consumption. Firstly, we present an overview of both traditional energy saving techniques and new developments in architectural approaches to energy-efficient processing. Secondly, we propose a picoMIPS architecture that serves as an architectural template for energy-efficient synthesis. As a case study, we show how the picoMIPS architecture can be tailored to an energy efficient execution of the DCT algorithm

Learning-based run-time power and energy management of multi/many-core systems: current and future trends

Multi/Many-core systems are prevalent in several application domains targeting different scales of computing such as embedded and cloud computing. These systems are able to fulfil the everincreasing performance requirements by exploiting their parallel processing capabilities. However, effective power/energy management is required during system operations due to several reasons such as to increase the operational time of battery operated systems, reduce the energy cost of datacenters, and improve thermal efficiency and reliability. This article provides an extensive survey of learning-based run-time power/energy management approaches. The survey includes a taxonomy of the learning-based approaches. These approaches perform design-time and/or run-time power/energy management by employing some learning principles such as reinforcement learning. The survey also highlights the trends followed by the learning-based run-time power management approaches, their upcoming trends and open research challenges

Dust Temperatures in the Infrared Space Observatory Atlas of Bright Spiral Galaxies

We examine far-infrared and submillimeter spectral energy distributions for galaxies in the Infrared Space Observatory Atlas of Bright Spiral Galaxies. For the 71 galaxies where we had complete 60-180 micron data, we fit blackbodies with lambda^-1 emissivities and average temperatures of 31 K or lambda^-2 emissivities and average temperatures of 22 K. Except for high temperatures determined in some early-type galaxies, the temperatures show no dependence on any galaxy characteristic. For the 60-850 micron range in eight galaxies, we fit blackbodies with lambda^-1, lambda-2, and lambda^-beta (with beta variable) emissivities to the data. The best results were with the lambda^-beta emissivities, where the temperatures were ~30 K and the emissivity coefficient beta ranged from 0.9 to 1.9. These results produced gas to dust ratios that ranged from 150 to 580, which were consistent with the ratio for the Milky Way and which exhibited relatively little dispersion compared to fits with fixed emissivities.Comment: AJ, 2003, in pres

Run-time performance and power optimization of parallel disparity estimation on many-core platforms

This paper investigates the use of many-core systems to execute the disparity estimation algorithm, used in stereo vision applications, as these systems can provide flexibility between performance scaling and power consumption. We present a learning-based run-time management approach which achieves a required performance threshold whilst minimizing power consumption through dynamic control of frequency and core allocation. Experimental results are obtained from a 61-core Intel Xeon Phi platform for the above investigation. The same performance can be achieved with an average reduction in power consumption of 27.8% and increased energy efficiency by 30.04% when compared to DVFS control alone without run-time management