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FASTA: Revisiting Fully Associative Memories in Computer Microarchitecture

Esteban Garzón, Robert Hanhan, Marco Lanuzza, Adam Teman, Leonid Yavits

2024IEEE Access17 citationsDOIOpen Access PDF

Abstract

Associative access is widely used in fundamental microarchitectural components, such as caches and TLBs. However, associative (or content addressable) memories (CAMs) have been traditionally considered too large, too energy-hungry, and not scalable, and therefore, have limited use in modern computer microarchitecture. This work revisits these presumptions and proposes an energy-efficient fully-associative tag array (FASTA) architecture, based on a novel complementary CAM (CCAM) bitcell. CCAM offers a full CMOS solution for CAM, removing the need for time- and energy-consuming precharge and combining the speed of NOR CAM and low energy consumption of NAND CAM. While providing better performance and energy consumption, CCAM features a larger area compared to state-of-the-art CAM designs. We further show how FASTA can be used to construct a novel aliasing-free, energy-efficient, Very-Many-Way Associative (VMWA) cache. Circuit-level simulations using 16nm FinFET technology show that a 128 kB FASTA-based 256-way 8-set associative cache is 28% faster and consumes 88% less energy-per-access than a same sized 8-way (256-set) SRAM based cache, while also providing aliasing-free operation. System-level evaluation performed on the Sniper simulator shows that the VMWA cache exhibits lower Misses Per Kilo Instructions (MPKI) for the majority of benchmarks. Specifically, the 256-way associative cache achieves 17.3%, 11.5%, and 1.2% lower average MPKI for L1, L2, and L3 caches, respectively, compared to a 16-way associative cache. The average IPC improvement for L1, L2, and L3 caches are 1.6%, 1.4%, and 0.2%, respectively.

Topics & Concepts

Computer scienceCacheAssociative propertyParallel computingMicroarchitectureCPU cacheContent-addressable memoryEnergy consumptionEmbedded systemScalabilitySet (abstract data type)Computer architectureComputer hardwareOperating systemArtificial neural networkArtificial intelligenceBiologyMathematicsProgramming languageEcologyPure mathematicsNetwork Packet Processing and OptimizationAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices
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