Multifunctional In‐Memory Analog‐to‐Digital Converter for Next‐Gen Compute‐in‐Memory Systems
Compute‐in‐memory (CIM) technology based on emerging nonvolatile memories (NVMs) has shown promise in enhancing artificial intelligence applications by integrating computation directly within NVM arrays. However, the efficiency of CIM systems is often curtailed by the substantial overhead that is ca...
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400594 |
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| author | Jiseong Im Jonghyun Ko Joon Hwang Jangsaeng Kim Wonjun Shin Ryun‐Han Koo Minkyu Park Sung‐Ho Park Woo Young Choi Jae‐Joon Kim Jong‐Ho Lee |
| author_facet | Jiseong Im Jonghyun Ko Joon Hwang Jangsaeng Kim Wonjun Shin Ryun‐Han Koo Minkyu Park Sung‐Ho Park Woo Young Choi Jae‐Joon Kim Jong‐Ho Lee |
| author_sort | Jiseong Im |
| collection | DOAJ |
| description | Compute‐in‐memory (CIM) technology based on emerging nonvolatile memories (NVMs) has shown promise in enhancing artificial intelligence applications by integrating computation directly within NVM arrays. However, the efficiency of CIM systems is often curtailed by the substantial overhead that is caused by traditional complementary metal‐oxide‐semiconductor (CMOS)‐based analog‐to‐digital converters (ADCs). Here, we report an in‐memory ADC (IMADC) that leverages NVMs to perform the dual functionalities of reference generation and voltage comparison, effectively minimizing the area occupancy and energy consumption, is reported. The IMADC not only significantly outperforms traditional ADCs but also enables the inherent processing of nonlinear activation functions such as the sigmoid function, which is required for neural networks. The IMADC‐based CIM system achieves software‐comparable accuracy in CIFAR‐10 image classification on the VGG‐9 network. The IMADC exhibits significantly reduced area occupancy (45 μm2) and energy consumption (29.6 fJ) compared to conventional CMOS‐based ADCs. The IMADC, compatible with various types of NVMs, demonstrates significant potential for enhancing the efficiency of CIM systems in terms of area occupancy and energy consumption. |
| format | Article |
| id | doaj-art-8fed5d306cd74fe285cdbab8dba83627 |
| institution | OA Journals |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-8fed5d306cd74fe285cdbab8dba836272025-08-20T01:55:22ZengWileyAdvanced Intelligent Systems2640-45672025-05-0175n/an/a10.1002/aisy.202400594Multifunctional In‐Memory Analog‐to‐Digital Converter for Next‐Gen Compute‐in‐Memory SystemsJiseong Im0Jonghyun Ko1Joon Hwang2Jangsaeng Kim3Wonjun Shin4Ryun‐Han Koo5Minkyu Park6Sung‐Ho Park7Woo Young Choi8Jae‐Joon Kim9Jong‐Ho Lee10Department of Electrical and Computer Engineering Inter‐University Semiconductor Research Center College of Engineering Seoul National University Seoul 08826 Republic of KoreaDepartment of Electrical and Computer Engineering Inter‐University Semiconductor Research Center College of Engineering Seoul National University Seoul 08826 Republic of KoreaDepartment of Electrical and Computer Engineering Inter‐University Semiconductor Research Center College of Engineering Seoul National University Seoul 08826 Republic of KoreaDepartment of Electrical and Computer Engineering Inter‐University Semiconductor Research Center College of Engineering Seoul National University Seoul 08826 Republic of KoreaDepartment of Semiconductor Convergence Engineering Sungkyunkwan University Suwon 16419 Republic of KoreaDepartment of Electrical and Computer Engineering Inter‐University Semiconductor Research Center College of Engineering Seoul National University Seoul 08826 Republic of KoreaDepartment of Electrical and Computer Engineering Inter‐University Semiconductor Research Center College of Engineering Seoul National University Seoul 08826 Republic of KoreaDepartment of Electrical and Computer Engineering Inter‐University Semiconductor Research Center College of Engineering Seoul National University Seoul 08826 Republic of KoreaDepartment of Electrical and Computer Engineering Inter‐University Semiconductor Research Center College of Engineering Seoul National University Seoul 08826 Republic of KoreaDepartment of Electrical and Computer Engineering Inter‐University Semiconductor Research Center College of Engineering Seoul National University Seoul 08826 Republic of KoreaDepartment of Electrical and Computer Engineering Inter‐University Semiconductor Research Center College of Engineering Seoul National University Seoul 08826 Republic of KoreaCompute‐in‐memory (CIM) technology based on emerging nonvolatile memories (NVMs) has shown promise in enhancing artificial intelligence applications by integrating computation directly within NVM arrays. However, the efficiency of CIM systems is often curtailed by the substantial overhead that is caused by traditional complementary metal‐oxide‐semiconductor (CMOS)‐based analog‐to‐digital converters (ADCs). Here, we report an in‐memory ADC (IMADC) that leverages NVMs to perform the dual functionalities of reference generation and voltage comparison, effectively minimizing the area occupancy and energy consumption, is reported. The IMADC not only significantly outperforms traditional ADCs but also enables the inherent processing of nonlinear activation functions such as the sigmoid function, which is required for neural networks. The IMADC‐based CIM system achieves software‐comparable accuracy in CIFAR‐10 image classification on the VGG‐9 network. The IMADC exhibits significantly reduced area occupancy (45 μm2) and energy consumption (29.6 fJ) compared to conventional CMOS‐based ADCs. The IMADC, compatible with various types of NVMs, demonstrates significant potential for enhancing the efficiency of CIM systems in terms of area occupancy and energy consumption.https://doi.org/10.1002/aisy.202400594analog‐to‐digital convertercompute‐in‐memoryflash thin‐film‐transistorhardware‐based artificial intelligenceneuromorphic computing |
| spellingShingle | Jiseong Im Jonghyun Ko Joon Hwang Jangsaeng Kim Wonjun Shin Ryun‐Han Koo Minkyu Park Sung‐Ho Park Woo Young Choi Jae‐Joon Kim Jong‐Ho Lee Multifunctional In‐Memory Analog‐to‐Digital Converter for Next‐Gen Compute‐in‐Memory Systems Advanced Intelligent Systems analog‐to‐digital converter compute‐in‐memory flash thin‐film‐transistor hardware‐based artificial intelligence neuromorphic computing |
| title | Multifunctional In‐Memory Analog‐to‐Digital Converter for Next‐Gen Compute‐in‐Memory Systems |
| title_full | Multifunctional In‐Memory Analog‐to‐Digital Converter for Next‐Gen Compute‐in‐Memory Systems |
| title_fullStr | Multifunctional In‐Memory Analog‐to‐Digital Converter for Next‐Gen Compute‐in‐Memory Systems |
| title_full_unstemmed | Multifunctional In‐Memory Analog‐to‐Digital Converter for Next‐Gen Compute‐in‐Memory Systems |
| title_short | Multifunctional In‐Memory Analog‐to‐Digital Converter for Next‐Gen Compute‐in‐Memory Systems |
| title_sort | multifunctional in memory analog to digital converter for next gen compute in memory systems |
| topic | analog‐to‐digital converter compute‐in‐memory flash thin‐film‐transistor hardware‐based artificial intelligence neuromorphic computing |
| url | https://doi.org/10.1002/aisy.202400594 |
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