前瞻科技 ›› 2022, Vol. 1 ›› Issue (3): 115-129.DOI: 10.3981/j.issn.2097-0781.2022.03.010
收稿日期:
2022-08-23
修回日期:
2022-09-02
出版日期:
2022-09-20
发布日期:
2022-11-04
通讯作者:
吴华强
作者简介:
姚鹏,助理研究员。研究方向为忆阻器阵列及其应用,存算一体宏电路设计,存算一体AI芯片架构和算法等。在Nature、Nature Communications等学术期刊和ISSCC、IEDM、VLSI等学术会议上发表多篇论文。电子信箱: yaop14@tsinghua.org.cn。基金资助:
YAO Peng(), SONG Changming, HU Yang, CAI Jian, YIN Shouyi, WU Huaqiang(
)
Received:
2022-08-23
Revised:
2022-09-02
Online:
2022-09-20
Published:
2022-11-04
Contact:
WU Huaqiang
摘要:
面向未来高算力芯片需求,分析了国内外高算力芯片发展趋势,提出由数据互连、单位晶体管提供的算力、晶体管密度和芯片面积构成的芯片算力表达式。介绍了未来高算力芯片发展的关键技术,并结合算力表达式论述相关技术如何发挥作用。从新材料、新器件、先进工艺、新架构、集成封装等角度出发,探讨了集成电路先进制造工艺、单片三维集成技术、领域专用架构、粗粒度可重构架构、存算一体技术、芯粒(Chiplet)技术和晶圆级集成等国内外发展现状及其对芯片算力的提升效果,并深入分析了各项技术的发展和挑战。结合中国高算力芯片现状和集成电路先进制程发展受限,提出从“架构+集成+系统”出发,探索实现高算力芯片的一体化自主可控创新路径,可以采用成熟制程,结合粗粒度可重构和存算一体新型架构,采用基于先进集成的芯粒技术实现总算力突破。
姚鹏, 宋昌明, 胡杨, 蔡坚, 尹首一, 吴华强. 高算力芯片未来技术发展途径[J]. 前瞻科技, 2022, 1(3): 115-129.
YAO Peng, SONG Changming, HU Yang, CAI Jian, YIN Shouyi, WU Huaqiang. Future Technical Development Approach for High Computing Power Chips[J]. Science and Technology Foresight, 2022, 1(3): 115-129.
代工厂 | 国家/地区 | 量产工艺节点/nm | 晶体管密度/(106 • mm-2) |
---|---|---|---|
台积电 | 中国台湾 | 5 | 196.6 |
中芯国际 | 中国大陆 | 14 | 30.0 |
英特尔 | 美国 | 7 | 106.1 |
三星 | 韩国 | 5 | 145.7 |
表1 全球主要集成电路制造厂的量产工艺节点及晶体管密度
代工厂 | 国家/地区 | 量产工艺节点/nm | 晶体管密度/(106 • mm-2) |
---|---|---|---|
台积电 | 中国台湾 | 5 | 196.6 |
中芯国际 | 中国大陆 | 14 | 30.0 |
英特尔 | 美国 | 7 | 106.1 |
三星 | 韩国 | 5 | 145.7 |
[1] | Hager G, Wellein G. Introduction to high performance computing for scientists and engineers[M]. Boca Raton: CRC Press, 2010. |
[2] |
Reuther A, Michaleas P, Jones M, et al. AI accelerator survey and trends[C]// 2021 IEEE High Performance Extreme Computing Conference (HPEC). Piscataway: IEEE Press, 2021, doi: 10.1109/HPEC49654.2021.9622867.
DOI |
[3] |
Luo Y, Mirabbasi S. A 30-fps 192×192 CMOS image sensor with per-frame spatial-temporal coded exposure for compressive focal-stack depth sensing[J]. IEEE Journal of Solid-State Circuits, 2022, 57(6): 1661-1672.
DOI URL |
[4] |
Waldrop M M. More than moore[J]. Nature, 2016, 530(7589): 144-148.
DOI URL |
[5] |
Shalf J. The future of computing beyond Moore,s law[J]. Philosophical Transactions of the Royal Society A, 2020, doi: 10.1098/rsta.2019.0061.
DOI |
[6] | 许海涛, 彭练矛. 碳基集成电路技术研究进展与展望[J]. 数据与计算发展前沿, 2021, 3(5): 4-27. |
[7] |
Yen A, Meiling H, Benschop J. Enabling manufacturing of sub-10 nm generations of integrated circuits with EUV lithography[C]// 2019 Electron Devices Technology and Manufacturing Conference (EDTM). Piscataway: IEEE Press, 2019, doi: 10.1109/EDTM.2019.8731058.
DOI |
[8] |
Hisamoto D, Wen-chin L, Kedzierski J, et al. FinFET-a self-aligned double-gate MOSFET scalable to 20 nm[J]. IEEE Transactions on Electron Devices, 2000, 47(12): 2320-2325.
DOI URL |
[9] |
Takato H, Sunouchi K, Okabe N, et al. High performance CMOS surrounding gate transistor (SGT) for ultra high density LSIs[C]// International Electron Devices Meeting. Piscataway: IEEE Press, 2002, doi: 10.1109/IEDM.1988.32796.
DOI |
[10] |
Singh N, Agarwal A, Bera L K, et al. High-performance fully depleted silicon nanowire (diameter/spl les/5 nm) gate-all-around CMOS devices[J]. IEEE Electron Device Letters, 2006, 27(5): 383-386.
DOI URL |
[11] | Aly M M S, Gao M, Hills G, et al. Energy-efficient abundant-data computing: The N3XT 1,000x[J]. Computer, 2015, 48(12): 24-33. |
[12] |
Shulaker M M, Hills G, Park R S, et al. Three-dimensional integration of nanotechnologies for computing and data storage on a single chip[J]. Nature, 2017, 547(7661): 74-78.
DOI URL |
[13] |
Bishop M D, Hills G, Srimani T, et al. Fabrication of carbon nanotube field-effect transistors in commercial silicon manufacturing facilities[J]. Nature Electronics, 2020, 3(8): 492-501.
DOI URL |
[14] |
Liu Y, Zhang J, Peng L M. Three-dimensional integration of plasmonics and nanoelectronics[J]. Nature Electronics, 2018, 1(12): 644-651.
DOI URL |
[15] |
Li Y, Tang J, Gao B, et al. Monolithic 3D integration of logic, memory and computing-in-memory for one-shot learning[C]// 2021 IEEE International Electron Devices Meeting. Piscataway: IEEE Press, 2022, doi: 10.1109/IEDM19574.2021.9720534.
DOI |
[16] | Choquette J, Gandhi W, Giroux O, et al. NVIDIA A100 tensor core GPU: Performance and innovation[J]. IEEE Micro, 2021, 41(2): 29-35. |
[17] | Elster A C, Haugdahl T A. Nvidia hopper GPU and grace CPU highlights[J]. Computing in Science & Engineering, 2022, 24(2): 95-100. |
[18] |
Tu F, Wu Z, Wang Y, et al. A 28 nm 15.59 µJ/token full-digital bitline-transpose CIM-based sparse transformer accelerator with pipeline/parallel reconfigurable modes[C]// 2022 IEEE International Solid-State Circuits Conference (ISSCC). Piscataway: IEEE Press, 2022, doi: 10.1109/ISSCC42614.2022.9731645.
DOI |
[19] |
Hennessy J L, Patterson D A. A new golden age for computer architecture[J]. Communications of the ACM, 2019, 62(2): 48-60.
DOI |
[20] | Liu L, Zhu J, Li Z, et al. A survey of coarse-grained reconfigurable architecture and design: Taxonomy, challenges, and applications[J]. ACM Computing Surveys (CSUR), 2019, 52(6): 1-39. |
[21] |
Tu F, Yin S, Ouyang P, et al. Deep convolutional neural network architecture with reconfigurable computation patterns[J]. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2017, 25(8): 2220-2233.
DOI URL |
[22] |
Burstein I. Nvidia data center processing unit (DPU) architecture[C]// 2021 IEEE Hot Chips 33 Symposium (HCS). Piscataway: IEEE Press, 2021, doi: 10.1109/HCS52781.2021.9567066.
DOI |
[23] |
Singh G, Chelini L, Corda S, et al. Near-memory computing: Past, present, and future[J]. Microprocessors and Microsystems, 2019, doi: 10.1016/j.micpro.2019.102868.
DOI |
[24] |
Zhang W, Gao B, Tang J, et al. Neuro-inspired computing chips[J]. Nature Electronics, 2020, 3(7): 371-382.
DOI URL |
[25] |
Knowles S. Graphcore[C]// 2021 IEEE Hot Chips 33 Symposium (HCS). Piscataway: IEEE Press, 2021, doi: 10.1109/HCS52781.2021.9567075.
DOI |
[26] |
Lauterbach G. The path to successful wafer-scale integration: The cerebras story[J]. IEEE Micro, 2021, 41(6): 52-57.
DOI URL |
[27] | Ke L, Zhang X, So J, et al. Near-memory processing in action: Accelerating personalized recommendation with AxDIMM[J]. IEEE Micro, 2021, 42(1): 116-127. |
[28] |
Niu D, Li S, Wang Y, et al. 184 QPS/W 64 Mb/mm2 3D logic-to-DRAM hybrid bonding with process-near-memory engine for recommendation system[C]// 2022 IEEE International Solid-State Circuits Conference (ISSCC). Piscataway: IEEE Press, 2022, doi: 10.1109/ISSCC42614.2022.9731694.
DOI |
[29] |
Lanza M, Sebastian A, Lu W D, et al. Memristive technologies for data storage, computation, encryption, and radio-frequency communication[J]. Science, 2022, 376(6597), doi: 10.1126/science.abj9979.
DOI |
[30] |
Yao P, Wu H, Gao B, et al. Fully hardware-implemented memristor convolutional neural network[J]. Nature, 2020, 577(7792): 641-646.
DOI URL |
[31] |
Liu Q, Gao B, Yao P, et al. 33.2 A fully integrated analog ReRAM based 78.4 TOPS/W compute-in-memory chip with fully parallel MAC computing[C]// 2020 IEEE International Solid-State Circuits Conference (ISSCC). Piscataway: IEEE Press, 2020, doi: 10.1109/ISSCC19947.2020.9062953.
DOI |
[32] | Xia J, Cheng C, Zhou X, et al. Kunpeng 920: The first 7-nm chiplet-based 64-Core ARM SoC for cloud services[J]. IEEE Micro, 2021, 41(5): 67-75. |
[33] |
Kim J, Murali G, Park H, et al. Architecture, chip, and package co-design flow for 2.5D IC design enabling heterogeneous IP reuse[C]// 2019 56th ACE/IEEE Design Automation Conference. Piscataway: IEEE Press, 2019, doi: 10.1109/TVLSI.2020.3015494.
DOI |
[34] |
Li T, Hou J, Yan J, et al. Chiplet heterogeneous integration technology—Status and challenges[J]. Electronics, 2020, doi: 10.3390/electronics9040670.
DOI |
[35] |
Ma X, Wang Y, Wang Y,, et al. Survey on chiplets: Interface, interconnect and integration methodology[J]. CCF Transactions on High Performance Computing, 2022, 4(5): 43-52.
DOI URL |
[36] |
Gomes W, Koker A, Stover P, et al. Ponte vecchio: A multi-tile 3D stacked processor for exascale comput- ing[C]// 2022 IEEE International Solid-State Circuits Conference (ISSCC). Piscataway: IEEE Press, 2022, doi: 10.1109/ISSCC42614.2022.9731673.
DOI |
[37] | 蒋剑飞, 王琴, 贺光辉, 等. Chiplet技术研究与展望[J]. 微电子学与计算机, 2022, 39(1): 1-6. |
[38] |
Douglas C, Wang C T, Hsia H. Foundry perspectives on 2.5D/3D integration and roadmap[C]// 2021 IEEE International Electron Devices Meeting (IEDM). Piscataway: IEEE Press, 2022, doi: 10.1109/IEDM19574.2021.9720568.
DOI |
[39] | 杨晖. 后摩尔时代Chiplet技术的演进与挑战[J]. 集成电路应用, 2020, 37(5): 52-54. |
[40] | 陈桂林, 王观武, 胡健, 等. Chiplet封装结构与通信结构综述[J]. 计算机研究与发展, 2022, 59(1): 22-30. |
[41] | Flack W, Flores G. Lithographic manufacturing techniques for wafer scale integration[C]// Proceedings International Conference on Wafer Scale Integration. Piscataway: IEEE Press, 2002: 4-13. |
[42] |
Cheng R, Bai J, Liao L, et al. High-frequency self-aligned graphene transistors with transferred gate stacks[J]. Proceedings of the National Academy of Sciences, 2012, 109(29): 11588-11592.
DOI URL |
[43] |
Wu F, Tian H, Shen Y, et al. Vertical MoS2 transistors with sub-1-nm gate lengths[J]. Nature, 2022, 603(7900): 259-264.
DOI URL |
[44] |
Si J, Zhong D, Xu H, et al. Scalable preparation of high-density semiconducting carbon nanotube arrays for high-performance field-effect transistors[J]. ACS Nano, 2018, 12(1): 627-634.
DOI PMID |
[45] |
Pitner G, Zhang Z, Lin Q, et al. Sub-0.5 nm interfacial dielectric enables superior electrostatics: 65 mV/dec top-gated carbon nanotube FETs at 15 nm gate length[C]// 2022 IEEE International Electron Devices Meeting (IEDM). Piscataway: IEEE Press, 2021, doi: 10.1109/IEDM13553.2020.9371899.
DOI |
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