Design of Energy Efficient Wireless Networks Using Dynamic Data Type Refinement Methodology

This paper presents a new perspective to the design of wireless networks using the proposed dynamic data type refinement methodology. In the forthcoming years, new portable devices will execute wireless network applications with extensive computational demands (2 – 30 GOPS) with low energy consumption demands (0.3 – 2 Watts). Nowadays, in such dynamic applications the dynamic memory subsystem is one of the main sources of energy consumption and it can heavily affect the performance of the whole system, if it is not properly managed. The main objective is to arrive at energy efficient realizations of the dominant dynamic data types of this dynamic memory subsystem. The simulation results in real case studies show that our methodology reduces energy consumption 50% on average

Dynamic Data Type Refinement Methodology for Systematic Performance-Energy Design Exploration of Network Applications

Network applications are becoming increasingly popular in the embedded systems domain requiring high performance, which leads to high energy consumption. In networks is observed that due to their inherent dynamic nature the dynamic memory subsystem is a main contributor to the overall energy consumption and performance. This paper presents a new systematic methodology, generating performance-energy trade-offs by implementing Dynamic Data Types (DDTs), targeting network applications. The proposed methodology consists of: (i) the application-level DDT exploration, (ii) the network-level DDT exploration and (iii) the Pareto-level DDT exploration. The methodology, supported by an automated tool, offers the designer a set of optimal dynamic data type design solutions. The effectiveness of the proposed methodology is tested on four representative real-life case studies. By applying the second step, it is proved that energy savings up to 80% and performance improvement up to 22% (compared to the original implementations of the benchmarks) can be achieved. Additional energy and performance gains can be achieved and a wide range of possible trade-offs among our Pareto-optimal design choices are obtained, by applying the third step. We achieved up to 93% reduction in energy consumption and up to 48% increase in performance