Exploration of Energy and Throughput Tradeoffs for Dataflow Networks

2026-03-31

AI Summary

This work addresses the challenge in self-powered streaming networks where dynamic power management, while energy-efficient, incurs switching delays that degrade throughput and hinder real-time signal processing. The paper presents the first cycle-based scheduling framework tailored to such networks, formulating a linear program to compute the maximum achievable throughput and a mixed-integer linear program to minimize energy consumption under throughput constraints. To efficiently explore the trade-off between energy and throughput, the authors introduce a novel “Hop and Skip” multi-objective search strategy that rapidly generates a high-quality Pareto frontier. Experimental results demonstrate that the proposed approach significantly accelerates design space exploration on both benchmark and random graphs, and in practical case studies, it achieves superior energy-throughput trade-offs compared to always-on or purely self-powered baselines.

Abstract

The introduction of dynamic power management strategies such as clock gating and power gating in dataflow networks has been shown to provide significant energy savings when applied during idle times. However, these strategies can also degrade throughput due to shutdown and wake-up delays. Such throughput degradations might be particularly detrimental to signal processing systems that require a guaranteed throughput. As a solution, this paper first contributes a linear-program formulation for finding a periodic maximal-throughput schedule of a given so-called self-powering dataflow network where actors, realized in hardware, are allowed to go to sleep whenever not being enabled to fire. Depending on which actors are allowed to power down, tradeoffs between throughput and energy savings can be obtained. As a second contribution, we propose a mixed-integer-linear-program formulation to determine a periodic schedule that satisfies a given throughput while minimizing the overall energy per period by identifying a respective set of actors that is allowed to power down in phases of idleness and which rather not. Finally, as a third contribution, we propose a multi-objective design-space exploration strategy called "Hop and Skip" to efficiently explore the Pareto front of energy and throughput solutions. Experimental evaluations on a set of existing benchmarks and randomly generated graphs witness significant exploration time reductions over a brute-force sweep. Finally, a real-world case study is elaborated, and we report on achievable energy savings and throughputs of the related dataflow network where (a) all actors are always-active, (b) all actors are self-powered, and (c) all optimal energy and throughput tradeoff points as found by the proposed design-space exploration strategy.

Problem

• dataflow networks
• energy-throughput tradeoff
• dynamic power management
• throughput guarantee
• power gating

Innovation

• dataflow networks
• energy-throughput tradeoff
• self-powering actors
• mixed-integer linear programming
• design-space exploration