An Iterative Algorithm for Battery-Aware Task Scheduling on Portable Computing Platforms

TL;DR

This paper introduces an iterative heuristic algorithm for scheduling tasks on battery-powered portable systems with FPGAs or scalable processors, optimizing for energy efficiency while meeting deadlines.

Contribution

It presents a novel iterative heuristic algorithm that selects task sequences and hardware configurations to minimize battery energy consumption under timing constraints.

Findings

Algorithm effectively reduces energy consumption in case studies.

Demonstrates practical applicability with a robotic arm controller example.

Provides a systematic approach for battery-aware task scheduling.

Abstract

In this work we consider battery powered portable systems which either have Field Programmable Gate Arrays (FPGA) or voltage and frequency scalable processors as their main processing element. An application is modeled in the form of a precedence task graph at a coarse level of granularity. We assume that for each task in the task graph several unique design-points are available which correspond to different hardware implementations for FPGAs and different voltage-frequency combinations for processors. It is assumed that performance and total power consumption estimates for each design-point are available for any given portable platfrom, including the peripheral components such as memory and display power usage. We present an iterative heuristic algorithm which finds a sequence of tasks along with an appropriate design-point for each task, such that a deadline is met and the amount of battery energy used is as small as possible. A detailed illustrative example along with a case study of a real-world application of a robotic arm controller which demonstrates the usefulness of our algorithm is also presented.