{"title":"基于铁电TCAM的低温超维内存计算","authors":"Shivendra Singh Parihar;Shubham Kumar;Swetaki Chatterjee;Girish Pahwa;Yogesh Singh Chauhan;Hussam Amrouch","doi":"10.1109/JXCDC.2025.3547797","DOIUrl":null,"url":null,"abstract":"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>$1\\times 8$ </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.","PeriodicalId":54149,"journal":{"name":"IEEE Journal on Exploratory Solid-State Computational Devices and Circuits","volume":"11 ","pages":"34-41"},"PeriodicalIF":2.0000,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10909519","citationCount":"0","resultStr":"{\"title\":\"Cryogenic Hyperdimensional In-Memory Computing Using Ferroelectric TCAM\",\"authors\":\"Shivendra Singh Parihar;Shubham Kumar;Swetaki Chatterjee;Girish Pahwa;Yogesh Singh Chauhan;Hussam Amrouch\",\"doi\":\"10.1109/JXCDC.2025.3547797\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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>$1\\\\times 8$ </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.\",\"PeriodicalId\":54149,\"journal\":{\"name\":\"IEEE Journal on Exploratory Solid-State Computational Devices and Circuits\",\"volume\":\"11 \",\"pages\":\"34-41\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2025-03-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10909519\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Journal on Exploratory Solid-State Computational Devices and Circuits\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10909519/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal on Exploratory Solid-State Computational Devices and Circuits","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10909519/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
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 (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 $1\times 8$ 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.