Scalable and Efficient Configuration of Time-Division Multiplexed Resources

TL;DR

This paper presents scalable methods for configuring time-division multiplexed resources in complex, multi-application systems to meet real-time constraints while optimizing overall performance.

Contribution

It introduces an optimal branch-and-price approach, a fast heuristic, and a comprehensive evaluation for resource configuration in shared multi-core systems.

Findings

Branch-and-price improves scalability over ILP

Heuristic provides near-optimal solutions quickly

Experimental results validate approach effectiveness

Abstract

Consumer-electronics systems are becoming increasingly complex as the number of integrated applications is growing. Some of these applications have real-time requirements, while other non-real-time applications only require good average performance. For cost-efficient design, contemporary platforms feature an increasing number of cores that share resources, such as memories and interconnects. However, resource sharing causes contention that must be resolved by a resource arbiter, such as Time-Division Multiplexing. A key challenge is to configure this arbiter to satisfy the bandwidth and latency requirements of the real-time applications, while maximizing the slack capacity to improve performance of their non-real-time counterparts. As this configuration problem is NP-hard, a sophisticated automated configuration method is required to avoid negatively impacting design time. The main contributions of this article are: (1) an optimal approach that takes an existing integer linear programming (ILP) model addressing the problem and wraps it in a branch-and-price framework to improve scalability. (2) A faster heuristic algorithm that typically provides near-optimal solutions. (3) An experimental evaluation that quantitatively compares the branch-and-price approach to the previously formulated ILP model and the proposed heuristic. (4) A case study of an HD video and graphics processing system that demonstrates the practical applicability of the approach.