Heterogeneous Integration Technologies for Artificial Intelligence Applications
The rapid advancement of artificial intelligence (AI) has been enabled by semiconductor-based electronics. However, the conventional methods of transistor scaling are not enough to meet the exponential demand for computing power driven by AI. This has led to a technological shift toward system-level...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Journal on Exploratory Solid-State Computational Devices and Circuits |
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| Online Access: | https://ieeexplore.ieee.org/document/10731842/ |
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| _version_ | 1850278685532225536 |
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| author | Madison Manley Ashita Victor Hyunggyu Park Ankit Kaul Mohanalingam Kathaperumal Muhannad S. Bakir |
| author_facet | Madison Manley Ashita Victor Hyunggyu Park Ankit Kaul Mohanalingam Kathaperumal Muhannad S. Bakir |
| author_sort | Madison Manley |
| collection | DOAJ |
| description | The rapid advancement of artificial intelligence (AI) has been enabled by semiconductor-based electronics. However, the conventional methods of transistor scaling are not enough to meet the exponential demand for computing power driven by AI. This has led to a technological shift toward system-level scaling approaches, such as heterogeneous integration (HI). HI is becoming increasingly implemented in many AI accelerator products due to its potential to enhance overall system performance while also reducing electrical interconnect delays and energy consumption, which are critical for supporting data-intensive AI workloads. In this review, we introduce current and emerging HI technologies and their potential for high-performance systems. We then survey recent industrial and research progress in 3-D HI technologies that enable high bandwidth systems and finally present the emergence of glass core packaging for high-performance AI chip packages. |
| format | Article |
| id | doaj-art-bf251253bbba455aacbc48cfd476e335 |
| institution | OA Journals |
| issn | 2329-9231 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal on Exploratory Solid-State Computational Devices and Circuits |
| spelling | doaj-art-bf251253bbba455aacbc48cfd476e3352025-08-20T01:49:23ZengIEEEIEEE Journal on Exploratory Solid-State Computational Devices and Circuits2329-92312024-01-0110899710.1109/JXCDC.2024.348495810731842Heterogeneous Integration Technologies for Artificial Intelligence ApplicationsMadison Manley0https://orcid.org/0000-0002-9051-7518Ashita Victor1https://orcid.org/0000-0002-0062-1128Hyunggyu Park2Ankit Kaul3https://orcid.org/0000-0003-0301-1349Mohanalingam Kathaperumal4https://orcid.org/0000-0001-6700-643XMuhannad S. Bakir5School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USASchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USASchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USASchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USASchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USASchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USAThe rapid advancement of artificial intelligence (AI) has been enabled by semiconductor-based electronics. However, the conventional methods of transistor scaling are not enough to meet the exponential demand for computing power driven by AI. This has led to a technological shift toward system-level scaling approaches, such as heterogeneous integration (HI). HI is becoming increasingly implemented in many AI accelerator products due to its potential to enhance overall system performance while also reducing electrical interconnect delays and energy consumption, which are critical for supporting data-intensive AI workloads. In this review, we introduce current and emerging HI technologies and their potential for high-performance systems. We then survey recent industrial and research progress in 3-D HI technologies that enable high bandwidth systems and finally present the emergence of glass core packaging for high-performance AI chip packages.https://ieeexplore.ieee.org/document/10731842/Artificial intelligence (AI)glass packagingheterogeneous integration (HI)packaging |
| spellingShingle | Madison Manley Ashita Victor Hyunggyu Park Ankit Kaul Mohanalingam Kathaperumal Muhannad S. Bakir Heterogeneous Integration Technologies for Artificial Intelligence Applications IEEE Journal on Exploratory Solid-State Computational Devices and Circuits Artificial intelligence (AI) glass packaging heterogeneous integration (HI) packaging |
| title | Heterogeneous Integration Technologies for Artificial Intelligence Applications |
| title_full | Heterogeneous Integration Technologies for Artificial Intelligence Applications |
| title_fullStr | Heterogeneous Integration Technologies for Artificial Intelligence Applications |
| title_full_unstemmed | Heterogeneous Integration Technologies for Artificial Intelligence Applications |
| title_short | Heterogeneous Integration Technologies for Artificial Intelligence Applications |
| title_sort | heterogeneous integration technologies for artificial intelligence applications |
| topic | Artificial intelligence (AI) glass packaging heterogeneous integration (HI) packaging |
| url | https://ieeexplore.ieee.org/document/10731842/ |
| work_keys_str_mv | AT madisonmanley heterogeneousintegrationtechnologiesforartificialintelligenceapplications AT ashitavictor heterogeneousintegrationtechnologiesforartificialintelligenceapplications AT hyunggyupark heterogeneousintegrationtechnologiesforartificialintelligenceapplications AT ankitkaul heterogeneousintegrationtechnologiesforartificialintelligenceapplications AT mohanalingamkathaperumal heterogeneousintegrationtechnologiesforartificialintelligenceapplications AT muhannadsbakir heterogeneousintegrationtechnologiesforartificialintelligenceapplications |