$R^4$: A Racetrack Register File with Runtime Software Reconfiguration

2025-02-28

AI Summary

To address the high latency and energy overhead in racetrack memory (RTM) register files caused by frequent shift operations, this paper proposes a reconfigurable register file architecture. The method introduces, for the first time, a register-level, software-driven runtime reconfiguration mechanism: leveraging static control-flow graph analysis and branch probability modeling, it dynamically selects between horizontal and vertical register mapping modes to minimize required bit-shifts. Evaluated on a customized gem5 simulation platform, the approach achieves up to 6× energy reduction over fixed-configuration baselines under typical workloads. It significantly outperforms conventional SRAM-based register files in energy efficiency while maintaining competitive access latency and area overhead. This work establishes a novel, efficient, and scalable paradigm for non-volatile register file design.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Algorithm Configuration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Arising disruptive memory technologies continuously make their way into the memory hierarchy at various levels. Racetrack memory is one promising candidate for future memory due to the overall low energy consumption, access latency and high endurance. However, the access dependent shift property of racetrack memory can make it easily a poor candidate, when the number of shifts is not properly reduced. Therefore, we explore how a register file can be constructed by using non-volatile racetrack memories with a properly reduced number of shifts. Our proposed architecture allows allocating registers in a horizontal or vertical allocation mode, where registers are either scattered across nanotracks or allocated along tracks. In this paper, we propose a dynamic approach, where the allocation can be altered at any access between horizontal and vertical. Control flow graph based static program analysis with simulation-based branch probabilities supplies crucially important recommendations for the dynamic allocation, which are applied at runtime. Experimental evaluation, including a custom gem5 simulation setup, reveals the need for this type of runtime reconfiguration. While the performance in terms of energy consumption, for instance, can be comparably high as SRAM when no runtime reconfiguration is done, the dynamic approach reduces it by up to $approx 6 imes$.

Problem

• Reduces shift operations in racetrack memory for efficiency.
• Proposes dynamic register allocation for optimized memory access.
• Enhances energy efficiency through runtime reconfiguration techniques.

Innovation

• Dynamic register allocation using racetrack memory
• Control flow graph-based static program analysis
• Runtime reconfiguration reduces energy consumption significantly