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arXiv · 2509.14041

A TRRIP Down Memory Lane: Temperature-Based Re-Reference Interval Prediction For Instruction Caching

Henry Kao·Nikhil Sreekumar·Prabhdeep Singh Soni·Ali Sedaghati·Fang Su·Bryan Chan·Maziar Goudarzi·Reza Azimi
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Abstract

Modern mobile CPU software pose challenges for conventional instruction cache replacement policies due to their complex runtime behavior causing high reuse distance between executions of the same instruction. Mobile code commonly suffers from large amounts of stalls in the CPU frontend and thus starvation of the rest of the CPU resources. Complexity of these applications and their code footprint are projected to grow at a rate faster than available on-chip memory due to power and area constraints, making conventional hardware-centric methods for managing instruction caches to be inadequate. We present a novel software-hardware co-design approach called TRRIP (Temperature-based Re-Reference Interval Prediction) that enables the compiler to analyze, classify, and transform code based on "temperature" (hot/cold), and to provide the hardware with a summary of code temperature information through a well-defined OS interface based on using code page attributes. TRRIP's lightweight hardware extension employs code temperature attributes to optimize the instruction cache replacement policy resulting in the eviction rate reduction of hot code. TRRIP is designed to be practical and adoptable in real mobile systems that have strict feature requirements on both the software and hardware components. TRRIP can reduce the L2 MPKI for instructions by 26.5% resulting in geomean speedup of 3.9%, on top of RRIP cache replacement running mobile code already optimized using PGO.

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Henry Kao, Nikhil Sreekumar, Prabhdeep Singh Soni, Ali Sedaghati, Fang Su, Bryan Chan, Maziar Goudarzi, Reza Azimi. 2025-09-17. A TRRIP Down Memory Lane: Temperature-Based Re-Reference Interval Prediction For Instruction Caching. https://doi.org/10.1145/3725843.3756110

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