A Fast-and-Effective Early-Stage Multi-level Cache Optimization Method Based on Reuse-Distance Analysis

TL;DR

This paper introduces a fast, practical method for optimizing multi-level cache sizes during early system design using reuse-distance analysis, significantly reducing computation time and aiding decision-making.

Contribution

It generalizes reuse distance analysis and develops a quick optimization approach for cache design, including a simplified analytical model for early-phase decision support.

Findings

Achieves 150 to 250 times faster optimization than traditional simulation methods.

Provides insights into cache parameter effects on performance and power.

Enables early-stage cache design decisions with minimal computational effort.

Abstract

In this paper, we propose a practical and effective approach allowing designers to optimize multi-level cache size at the early system design phase. Our key contribution is to generalize the reuse distance analysis method and develop an effective and practical cache design optimization approach. We adopt a simple scanning search method to locate optimal cache solutions in terms of cache size, power consumption, or average data access delay. The proposed approach is particularly useful for early-phase system designers and is verified to be 150 to 250 times faster than the traditional simulation-based approach. In addition, we also introduce a simplified analytical model and provide designers insights about how cache design parameters may affect the expected results. As a result, designers can make an adequate decision in the early system design phase.