A Design Approach for Real-Time Embedded Systems with Energy and Code Size Constraints

Real-time embedded systems often have multiple resource constraints such as energy and code size constraints. Traditionally, techniques for reducing energy consumption for real-time embedded systems have been developed without considering code size constraints, whereas code size reduction techniques have been developed without considering energy constraints. There, however, is a tradeoff relationship between reducing dynamic energy consumption and reducing code size for real-time embedded systems. Therefore, reducing code size may result in increasing energy consumption. In this paper, we present a triple-tradeoff relationship among code size, execution time, and energy consumption and then address the code size minimization problem while considering simultaneously the energy constraints and the real-time requirements of embedded systems. We formulate such an optimization problem and prove this optimization problem is NP-hard. Given the difficulty of finding the optimal solution to the problem, we then propose four heuristic algorithms to find sub-optimal solutions and evaluate their performance through simulations

Observation and analysis of low temperature leak characteristics of the O-ring for hydrogen electric vehicles

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An exact stochastic analysis of priority-driven periodic real-time systems and its approximations.

Abstract This paper describes a stochastic analysis framework which computes the response time distribution and the deadline miss probability of individual tasks, even for systems with a maximum utilization greater than one. The framework is uniformly applied to fixed-priority and dynamic-priority systems and can handle tasks with arbitrary relative deadlines and execution time distributions

An Accurate Instruction-Level Energy Consumption Model for Embedded RISC Processors

Energy consumption of software is becoming an increasingly important issue in designing mobile embedded systems where batteries are used as the main power source. As a consequence, recently, a number of promising techniques have been proposed to optimize software for reduced energy consumption. Such low-power software techniques require an energy consumption model that can be used to estimate or predict the energy consumed by software. We propose a technique to derive an accurate energy consumption model at the instruction level, combining an empirical method and a statistical analysis technique. The result of the proposed approach is given by a model equation that characterizes energy behavior of software based on the properties of the instructions. Experimental results show that the model equation can accurately estimate the energy consumption of random instruction sequences, with an average error of 2.5 %. Keywords Low-power systems, instruction-level energy model, regression analysis 1

Embedded System Design Framework for Minimizing Code Size and Guaranteeing Real-Time Requirements

In addition to real-time requirements, the program code size is a critical design factor for real-time embedded systems. To take advantage of the code size vs. execution time tradeoff provided by reduced bit-width instructions, we propose a design framework that transforms the system constraints into task parameters guaranteeing a set of requirements. The goal of our design framework is to derive the temporal parameters and the code size parameter of each task in such a way that they collectively guarantee the system end-to-end timing requirements while the system code size is minimized. Our design framework is based on asynchronous periodic tasks with pre-period deadlines under EDF scheduling. For schedulability analysis, we present a new feasibility condition that can be more efficiently evaluated than existing ones. When the code size vs. execution time tradeoff can be safely approximated as linear functions, the minimization problem becomes a linear programming problem. However, when the tradeoff is given by a table of possible (code size, execution time) pairs, the problem becomes NP-hard. We provide three heuristic algorithms that can find sub-optimal solutions and evaluate their performance with simulation results