Cryo-CACTI: Cryogenic-Aware CACTI for Cache Modeling Down to 10K in Advanced 7nm FinFETs

Cryogenic circuits are currently employed in fields such as quantum computing, particle detectors, magnetic resonance imaging, and space applications. While cryogenic circuits are being researched, there is limited work on designing cryogenic caches at temperatures below 77K. Moreover, there is no tool to estimate the delay, power, and area of cryogenic caches at advanced technology nodes. Our research focuses on the development of cryogenic caches tailored for the 7nm technology node, operating at 10K. However, a key challenge is the lack of cryogenic measurement data, especially in recent technologies. Consequently, through conducting our own FinFET transistor measurements, we calibrate cryogenic transistor models at 10K. With the 7nm cryogenic transistor data, we model <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cryo-CACTI</i> for cryogenic caches (due to cache’s vital role in improving performance and their considerable share in area and power of the processor). Using Cryo-CACTI, our evaluation reveals considerable improvements in the energy efficiency (up to 99%) of cryogenic caches of larger sizes compared to the caches at room temperature (300K). Additionally, we explore alternative cache configurations at circuit-level to optimize cryogenic operation. Furthermore, we use Cryo-CACTI to explore the performance/energy consumption of cryogenic caches while simulating workloads such as SPEC CPU2017 and machine learning via neural networks. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cryo-CACTI is available for download at <uri>https://github.com/marg-tools/Cryo-CACTI</uri>

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Publication Details

Journal
IEEE Transactions on Computers
Published
2025-05-05
DOI
https://doi.org/10.1109/tc.2025.3566899
Citations
1
Primary Topic
Semiconductor materials and devices
Type
article
Field-Weighted Citation Impact
0.73
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article

Cryo-CACTI: Cryogenic-Aware CACTI for Cache Modeling Down to 10K in Advanced 7nm FinFETs

Divya Praneetha Ravipati, Preeti Ranjan Panda, Victor M. van Santen, Shivendra Singh Parihar et al.
1 citations
IEEE Transactions on Computers
Semiconductor materials and devices
0.73
article

Cryo-CACTI: Cryogenic-Aware CACTI for Cache Modeling Down to 10K in Advanced 7nm FinFETs

Divya Praneetha Ravipati, Preeti Ranjan Panda, Victor M. van Santen, Shivendra Singh Parihar, Hussam Amrouch, Yogesh Singh Chauhan
article en
1 citations

Abstract

Cryogenic circuits are currently employed in fields such as quantum computing, particle detectors, magnetic resonance imaging, and space applications. While cryogenic circuits are being researched, there is limited work on designing cryogenic caches at temperatures below 77K. Moreover, there is no tool to estimate the delay, power, and area of cryogenic caches at advanced technology nodes. Our research focuses on the development of cryogenic caches tailored for the 7nm technology node, operating at 10K. However, a key challenge is the lack of cryogenic measurement data, especially in recent technologies. Consequently, through conducting our own FinFET transistor measurements, we calibrate cryogenic transistor models at 10K. With the 7nm cryogenic transistor data, we model <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cryo-CACTI</i> for cryogenic caches (due to cache’s vital role in improving performance and their considerable share in area and power of the processor). Using Cryo-CACTI, our evaluation reveals considerable improvements in the energy efficiency (up to 99%) of cryogenic caches of larger sizes compared to the caches at room temperature (300K). Additionally, we explore alternative cache configurations at circuit-level to optimize cryogenic operation. Furthermore, we use Cryo-CACTI to explore the performance/energy consumption of cryogenic caches while simulating workloads such as SPEC CPU2017 and machine learning via neural networks. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cryo-CACTI is available for download at <uri>https://github.com/marg-tools/Cryo-CACTI</uri>

IEEE Transactions on ComputersVol. 74(8)
Technical University of Munich (DE), Indian Institute of Technology Delhi (IN), Indian Institute of Technology Kanpur (IN)
Openalex Percentile: Top 29%
Semiconductor materials and devices
0.73
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