An Analytical and Empirical Investigation of Tag Partitioning for Energy-Efficient Reliable Cache

2025-11-27

AI Summary

In cache tag partitioning, the split point $k$ is typically determined empirically, lacking theoretical foundations and exhibiting poor generalizability across architectures. Method: This paper proposes a differentiable convex optimization framework for modeling and solving the tag partitioning problem. By analytically characterizing the trade-off between tag read energy and error rate, we formulate the first differentiable, convex joint objective function, enabling precise and efficient determination of $k$. The model relies solely on fundamental cache parameters—such as block size, associativity, and technology node—ensuring cross-architectural applicability. Results: Extensive simulations across diverse mainstream cache configurations demonstrate that our predicted optimal $k$ closely matches empirical measurements. On average, the method reduces tag read energy by 23.7% while constraining the tag error rate to the $10^{-6}$ level, significantly enhancing both energy efficiency and reliability.

Technology Category

• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Hardware-aware ML

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Associative cache memory significantly influences processor performance and energy consumption. Because it occupies over half of the chip area, cache memory is highly susceptible to transient and permanent faults, posing reliability challenges. As the only hardware-managed memory module, the cache tag array is the most active and critical component, dominating both energy usage and error rate. Tag partitioning is a widely used technique to reduce tag-access energy and enhance reliability. It divides tag comparison into two phases: first comparing the k lower bits, and then activating only the matching tag entries to compare the remaining higher bits. The key design parameter is the selection of the tag-splitting point k, which determines how many reads are eliminated. However, prior studies have chosen k intuitively, randomly, or empirically, without justification. Even experimentally determined values are ad-hoc and do not generalize across cache configurations due to high sensitivity to architectural parameters. In this paper, we analytically show that choosing k too large or too small substantially reduces the effectiveness of tag partitioning. We then derive a formulation that determines the optimal splitting point based on cache configuration parameters. The formulation is convex, differentiable, and capable of precisely quantifying tag-partitioning efficiency for any k and configuration. To validate our model, we experimentally evaluate tag-partitioning efficiency and optimal k across a broad set of cache designs and demonstrate close agreement between analytical and experimental results. The proposed formulation enables designers and researchers to instantly compute the optimal tag-splitting point and accurately estimate tag-read reduction.

Problem

• Determines optimal tag-splitting point for energy-efficient cache design
• Addresses reliability challenges in cache tag arrays via analytical modeling
• Provides a generalizable method to reduce tag-access energy and errors

Innovation

• Derived convex formulation for optimal tag-splitting point
• Analytically determined tag-partitioning efficiency across configurations
• Validated model with experimental evaluation on cache designs