# Energy-Aware Scheduling of Task Graphs with Imprecise Computations and End-to-End Deadlines

## TL;DR

This paper introduces an energy-aware scheduling method for task graphs with imprecise computations, optimizing quality of service within deadlines and energy limits, validated by MILP and experiments on multiprocessor platforms.

## Contribution

It proposes a novel scheduling approach that accounts for imprecise inputs and provides optimal solutions via MILP for energy-efficient task graph management.

## Key findings

- Feasible schedules can use less than 50% of the minimum energy for precise execution.
- The method effectively balances energy consumption and output quality.
- Experimental results validate the approach on randomly generated task graphs.

## Abstract

Imprecise computations provide an avenue for scheduling algorithms developed for energy-constrained computing devices by trading off output quality with the utilization of system resources. This work proposes a method for scheduling task graphs with potentially imprecise computations, with the goal of maximizing the quality of service subject to a hard deadline and an energy bound. Furthermore, for evaluating the efficacy of the proposed method, a mixed integer linear program formulation of the problem, which provides the optimal reference scheduling solutions, is also presented. The effect of potentially imprecise inputs of tasks on their output quality is taken into account in the proposed method. Both the proposed method and MILP formulation target multiprocessor platforms. Experiments are run on 10 randomly generated task graphs. Based on the obtained results, for some cases, a feasible schedule of a task graph can be achieved with the energy consumption less than 50% of the minimum energy required for scheduling all tasks in that task graph completely precisely.