Cryogenic Hyperdimensional In-Memory Computing Using Ferroelectric TCAM
Cryogenic operations of electronics present a significant step forward to achieve huge demand of in-memory computing (IMC) for high-performance computing, quantum computing, and military applications. Ferroelectric (FE) is a promising candidate to develop the complementary metal oxide semiconductor...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal on Exploratory Solid-State Computational Devices and Circuits |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10909519/ |
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| Summary: | Cryogenic operations of electronics present a significant step forward to achieve huge demand of in-memory computing (IMC) for high-performance computing, quantum computing, and military applications. Ferroelectric (FE) is a promising candidate to develop the complementary metal oxide semiconductor (CMOS)-compatible nonvolatile memories. Hence, in this work, we investigate the effectiveness of IMC using emerging FE technology at the 5-nm technology node. To achieve that, we begin by characterizing commercial 5-nm fin field-effect transistors (FinFETs) from room temperature (300 K) down to cryogenic temperature (10 K). Then, we carefully calibrate the first industry-standard cryogenic-aware compact model [Berkeley Short-channel IGFET Model-Common Multi-Gate (BSIM-CMG)] to accurately reproduce the measurements. Afterward, we use the Preisach-model-based approach to incorporate the impact of FE within the BSIM-CMG model framework using the measurements from FE capacitor to realize ferroelectric fin field-effect transistors (Fe-FinFETs) operating from 300 down to 10 K. Then, as proof of concept, we focus on <inline-formula> <tex-math notation="LaTeX">$1\times 8$ </tex-math></inline-formula> ternary content addressable memory (TCAM) array that is used to perform language classification and voice recognition using brain-inspired hyperdimensional IMC. Our comprehensive analysis spans from investigating the delay, power, and energy efficiency of TCAM-based IMC all the way up to calculating error probabilities in which we compare the figure of merits obtained from the emerging Fe-FinFET against classical FinFET-based IMC. We reveal that cryogenic temperatures lead to the worst performance in Fe-FinFET-based TCAM. Hence, we have also proposed solutions to improve the cryogenic performance of Fe-FinFET-based TCAM. |
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| ISSN: | 2329-9231 |