Article(id=1315718827862769755, tenantId=1146029695717560320, journalId=1146032081894723586, issueId=1315718825799168011, articleNumber=null, orderNo=null, doi=10.3981/j.issn.2097-0781.20260023, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1783958400000, receivedDateStr=2026-07-14, revisedDate=1786291200000, revisedDateStr=2026-08-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1791621413435, onlineDateStr=2026-10-10, pubDate=1789833600000, pubDateStr=2026-09-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791621413435, onlineIssueDateStr=2026-10-10, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791621413435, creator=system, updateTime=1791621413435, updator=system, issue=Issue{id=1315718825799168011, tenantId=1146029695717560320, journalId=1146032081894723586, year='2026', volume='5', issue='3', pageStart='4', pageEnd='140', issueExtLink='null', onlineDate='null', pubDate='1789833600000', pubDateStr='2026-09-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1791621412943, creator='system', updateTime=1791623747519, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1315728617838068612, tenantId=1146029695717560320, journalId=1146032081894723586, issueId=1315718825799168011, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1315728617838068613, tenantId=1146029695717560320, journalId=1146032081894723586, issueId=1315718825799168011, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=70, endPage=78, ext={EN=ArticleExt(id=1315718828944900188, articleId=1315718827862769755, tenantId=1146029695717560320, journalId=1146032081894723586, language=EN, title=Interpreting Moore’s Law and the τ Scaling Law: A Path to Performance Enhancement from Spatial Compression to Temporal Compression, columnId=1315718827791462412, journalTitle=Science and Technology Foresight, columnName=Sci-Tech Focus, runingTitle=null, highlight=null, articleAbstract=

Moore’s law has long driven the growth of electronic computing performance through geometric transistor scaling and increased integration density. With the breakdown of Dennard scaling and the increasing prominence of power, interconnect, memory, and thermal constraints, semiconductor performance improvement is expanding from device-dominated scaling to multilevel temporal optimization across circuits, chips, and systems. This paper employs matter, energy, structure, and time from cognitive physics as a conceptual framework, regards chips and computing systems as the material substrate of machine cognition, and examines the intrinsic continuity between Moore’s law and the τ scaling law. The analysis shows that Moore’s law primarily delivers performance, energy-efficiency, and cost benefits through reduced physical dimensions and increased spatial density, whereas the Tau scaling law promotes coordinated optimization of critical paths, data movement, communication synchronization, and end-to-end execution time through logic folding, local interconnection, hardware-software co-design, and system interconnect reconfiguration. The transition from Moore’s law to the Tau scaling law is therefore not the replacement of one law by another, but an expansion of semiconductor performance optimization from geometry-dominated scaling toward the parallel development of spatial and temporal scaling. Matter provides the physical carrier, energy drives state transitions, structure organizes the paths of information and energy, and time enables computational processes to unfold in continuous temporal sequences. The coupling of these four elements offers a unified interpretive perspective for understanding post-Moore chip architectures, intelligent computing systems, and their constraints in terms of energy consumption, thermal dissipation, and reliability.

, authors=Deyi LI¹, Nan HE^{2, *}, authorsList=Deyi LI, Nan HE, authorCompany=null, correspAuthors=Nan HE, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright © 2026 Science and Technology Foresight. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1315718831587311714, articleId=1315718827862769755, tenantId=1146029695717560320, journalId=1146032081894723586, language=CN, title=解释摩尔定律和韬定律, columnId=1315718827976011789, journalTitle=前瞻科技, columnName=科技焦点, runingTitle=null, highlight=null, articleAbstract=

摩尔定律通过晶体管几何缩微和集成度提升长期推动电子计算效能增长。随着登纳德缩放失效，以及功耗、互连、存储和散热约束显性化，半导体效能提升正在由器件尺度主导，拓展到电路、芯片和系统的多层级时间优化。文章以认知物理学的物质、能量、结构、时间四要素为概念框架，将芯片和计算系统视为机器认知的物质硬构体，分析摩尔定律与韬定律之间的内在连续性。研究表明，摩尔定律主要通过物质尺度下降和空间结构增密释放性能、能效与成本红利；韬定律则通过逻辑折叠、就近互连、软硬协同和系统互联重构，推动关键路径、数据搬运、通信同步及端到端执行时间的协同优化。从摩尔定律到韬定律，并非一种规律取代另一种规律，而是半导体效能优化由单一几何缩微主导走向几何缩微与多层级时间优化并行的维度拓展。物质提供载体，能量驱动状态变化，结构组织信息和能量的路径，时间使计算过程形成连续时序；四要素的耦合为理解后摩尔时代芯片架构、智能计算系统及其能耗、散热和可靠性约束提供了统一的解释视角。

, authors=李德毅¹, 赫南^{2, *}, authorsList=李德毅, 赫南, authorCompany=null, correspAuthors=赫南, authorNote=, correspAuthorsNote=, copyrightStatement=版权所有 © 2026 前瞻科技编辑部, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=53yysBIKFJAkA73xd5FZRQ==, magXml=rOwKWEC5sJTxK5qbTKxvow==, pdfUrl=null, pdf=6cQnn5+D6SFZ8of89q7npA==, pdfFileSize=2966475, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=R05mxsMvQJCfJnuWPJXvnA==, mapNumber=null, fund=null)}, authors=[Author(id=1315718832078045290, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1315718827862769755, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=lidy@cae.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1315718832174514284, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1315718827862769755, authorId=1315718832078045290, language=EN, stringName=Deyi LI, firstName=Deyi, middleName=null, lastName=LI, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹Institute of Systems Engineering, Academy of Military Sciences, Beijing 100840, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1315718832250011757, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1315718827862769755, authorId=1315718832078045290, language=CN, stringName=李德毅, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹军事科学院系统工程研究院, 北京 100840, bio={"img":"VS5PbETjruuAjaGyQ6SNMg==","content":"

李德毅，中国工程院院士，研究员。中国人工智能学会和中国指挥和控制学会名誉理事长。主要从事计算机工程、不确定性人工智能、数据挖掘、知识发现、智能驾驶、认知物理学和认知工程研究。电子信箱：lidy@cae.cn

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李德毅，中国工程院院士，研究员。中国人工智能学会和中国指挥和控制学会名誉理事长。主要从事计算机工程、不确定性人工智能、数据挖掘、知识发现、智能驾驶、认知物理学和认知工程研究。电子信箱：lidy@cae.cn

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赫南，中英人寿保险有限公司AI架构师。主要从事不确定性人工智能、复杂网络及人工智能技术架构研究。电子信箱：henan@vip.qq.com。

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赫南，中英人寿保险有限公司AI架构师。主要从事不确定性人工智能、复杂网络及人工智能技术架构研究。电子信箱：henan@vip.qq.com。

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Boundaries and relationships of concepts related to spatial and temporal optimization

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概念	文章定义	主要尺度	与其他概念的关系
空间压缩	器件、连接、功能和系统在有限空间中的结构化收敛	器件至系统	用于概括电子计算技术长期空间演进的跨层级概念
几何缩微	晶体管和互连特征尺寸随工艺代际缩小	器件、工艺	空间压缩在半导体制造中的主要形式
时间压缩	减少状态转换、访问、通信和任务完成所需时间	器件至系统	用于统摄不同层级时间优化的分析概念
时间缩微	某一已定义时间指标随技术代际持续下降	特定层级	必须说明指标、工作负载和测量条件
τ缩微	韬定律提出的跨层级时间优化技术原则	器件至系统	不是单一物理常数，尚待工程和产业验证

), ArticleFig(id=1315718835618037890, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1315718827862769755, language=CN, label=表1, caption=

空间与时间优化相关概念的边界和关系

, figureFileSmall=null, figureFileBig=null, tableContent=

概念	文章定义	主要尺度	与其他概念的关系
空间压缩	器件、连接、功能和系统在有限空间中的结构化收敛	器件至系统	用于概括电子计算技术长期空间演进的跨层级概念
几何缩微	晶体管和互连特征尺寸随工艺代际缩小	器件、工艺	空间压缩在半导体制造中的主要形式
时间压缩	减少状态转换、访问、通信和任务完成所需时间	器件至系统	用于统摄不同层级时间优化的分析概念
时间缩微	某一已定义时间指标随技术代际持续下降	特定层级	必须说明指标、工作负载和测量条件
τ缩微	韬定律提出的跨层级时间优化技术原则	器件至系统	不是单一物理常数，尚待工程和产业验证

), ArticleFig(id=1315718835701923971, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1315718827862769755, language=EN, label=Table 2, caption=

Representative metrics and boundaries of multilevel temporal optimization

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层级	代表性时间指标	单位	主要决定因素	评价边界
器件级	开关时间、RC时间常数	ps，ns	材料、器件结构、寄生参数和负载	需在给定器件与测试条件下比较
电路级	关键路径延迟、时钟周期	ps，ns	逻辑深度、布线长度、扇出和时序收敛	受具体电路和工作频率约束
芯片级	存储访问、片上数据搬运和任务执行时间	ns，μs，ms	缓存层次、存储结构、数据流和软硬协同	需给出任务与数据规模
系统级	芯片间通信、同步和端到端时延	μs，ms，s	互联拓扑、协议、调度、网络和工作负载	不能以单一器件τ替代
结果指标	吞吐量、带宽和单位时间有效工作量	bit/s，任务数/s	上述多层级因素的综合结果	不是时间常数，应与延迟分开评价

), ArticleFig(id=1315718835802587268, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1315718827862769755, language=CN, label=表2, caption=

多层级时间优化的代表性指标及其边界

, figureFileSmall=null, figureFileBig=null, tableContent=

层级	代表性时间指标	单位	主要决定因素	评价边界
器件级	开关时间、RC时间常数	ps，ns	材料、器件结构、寄生参数和负载	需在给定器件与测试条件下比较
电路级	关键路径延迟、时钟周期	ps，ns	逻辑深度、布线长度、扇出和时序收敛	受具体电路和工作频率约束
芯片级	存储访问、片上数据搬运和任务执行时间	ns，μs，ms	缓存层次、存储结构、数据流和软硬协同	需给出任务与数据规模
系统级	芯片间通信、同步和端到端时延	μs，ms，s	互联拓扑、协议、调度、网络和工作负载	不能以单一器件τ替代
结果指标	吞吐量、带宽和单位时间有效工作量	bit/s，任务数/s	上述多层级因素的综合结果	不是时间常数，应与延迟分开评价

), ArticleFig(id=1315718835886473349, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1315718827862769755, language=EN, label=Table 3, caption=

Scales, mechanisms, and limitations of spatial and temporal optimization cases

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案例	作用尺度	主要优化对象	关键评价指标	机制与局限
主频提升	处理器、电路	时钟周期	频率、周期、功耗	缩短局部机器节拍；受电压、功耗和散热限制
逻辑折叠	电路、芯片	关键路径和数据邻近性	路径延迟、功耗、面积、结温	结构重排可能缩短互连；仍需工艺和实测验证
光互联	芯片至系统	数据传输和通信	带宽密度、能耗/bit、端到端时延	适合高带宽互联；受光电转换、封装和热调谐约束
异构协同	芯片、系统	计算−存储−通信等待	端到端时延、吞吐、利用率和能效	依赖拓扑、协议、调度、软件和工作负载
脑皮层折叠	生物组织	皮层表面积与连接布局	生物形态与连接指标	提供结构组织启示；与芯片工程机制不同，需避免直接类比

), ArticleFig(id=1315718835961970822, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1315718827862769755, language=CN, label=表3, caption=

空间与时间优化案例的作用尺度、机制及局限

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案例	作用尺度	主要优化对象	关键评价指标	机制与局限
主频提升	处理器、电路	时钟周期	频率、周期、功耗	缩短局部机器节拍；受电压、功耗和散热限制
逻辑折叠	电路、芯片	关键路径和数据邻近性	路径延迟、功耗、面积、结温	结构重排可能缩短互连；仍需工艺和实测验证
光互联	芯片至系统	数据传输和通信	带宽密度、能耗/bit、端到端时延	适合高带宽互联；受光电转换、封装和热调谐约束
异构协同	芯片、系统	计算−存储−通信等待	端到端时延、吞吐、利用率和能效	依赖拓扑、协议、调度、软件和工作负载
脑皮层折叠	生物组织	皮层表面积与连接布局	生物形态与连接指标	提供结构组织启示；与芯片工程机制不同，需避免直接类比

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