Article(id=1309639862664389313, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639861922005993, articleNumber=null, orderNo=null, doi=10.19822/j.cnki.1671-6329.20240293, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172075191, onlineDateStr=2026-09-23, pubDate=1788537600000, pubDateStr=2026-09-05, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172075191, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172075191, creator=13701087609, updateTime=1790172075191, updator=13701087609, issue=Issue{id=1309639861922005993, tenantId=1146029695717560320, journalId=1189645257101713411, year='2026', volume='', issue='9', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1788537600000', pubDateStr='2026-09-05', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172075014, creator='13701087609', updateTime=1790233557151, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309897736787427641, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639861922005993, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309897736787427642, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639861922005993, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=13, endPage=21, ext={EN=ArticleExt(id=1309639862869910210, articleId=1309639862664389313, tenantId=1146029695717560320, journalId=1189645257101713411, language=EN, title=Research on the Development Trend of Automotive AI Chips, columnId=1309639862555345898, journalTitle=Automotive Digest, columnName=Special Topic on Automotive Chip Technology, runingTitle=null, highlight=null, articleAbstract=
To clarify the current state of the automotive AI chip industry, address technological pain points, and identify the future development trends of the industry, while supporting the domestic automotive AI chip industry's independent and high-quality development, this focuses on the core application scenarios of AI chips in intelligent connected vehicles, such as smart driving perception, decision-making, control, and smart cockpit interaction. It systematically reviews the core requirements of intelligent connected vehicles for AI chips, including high computing power, low power consumption, high security, and high reliability. At the same time, it analyzes the technology layouts and product iterations of mainstream domestic and international automotive AI chip companies like Qualcomm, NVIDIA, Tesla, Horizon, and SiEngine Technology, and, based on the current industry situation, summarizes the main technical bottlenecks and solutions for automotive AI chips in areas like memory bandwidth and packaging processes. On this basis, it assesses the future development trends of automotive AI chips in terms of domain fusion, heterogeneous architecture integration, computing power growth, the implementation of computing-storage integration technology, and advanced packaging. The goal is to provide a reference and inspiration for the technological research and development, product iteration, industrial layout and relevant research of vehicle-mounted AI chips.
, authors=Yang Liu, Qiang Wang, Rui Bao, Wenchao Mei, Kai Zhang, authorsList=Yang Liu, Qiang Wang, Rui Bao, Wenchao Mei, Kai Zhang, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, 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=1309639863062848195, articleId=1309639862664389313, tenantId=1146029695717560320, journalId=1189645257101713411, language=CN, title=汽车AI芯片发展趋势研究, columnId=1309639862702146540, journalTitle=汽车文摘, columnName=汽车芯片技术专题, runingTitle=null, highlight=null, articleAbstract=
为了系统掌握汽车AI芯片产业发展现状、破解技术发展痛点、把握行业未来演进方向,助力国内汽车AI芯片产业自主化、高质量发展,聚焦AI芯片在智能网联汽车中的智能驾驶感知、决策、控制及智能座舱交互等核心应用场景,系统梳理智能网联汽车对AI芯片高算力、低功耗、高安全、高可靠等核心需求。同时分析国内外高通、NVIDIA、特斯拉、地平线、芯擎等主流汽车AI芯片企业的技术布局与产品迭代,结合当前行业发展现状,总结提炼了汽车AI芯片在内存带宽、封装工艺等方面存在的核心技术瓶颈与解决方法。在此基础上,梳理出汽车AI芯片在域融合、异构架构集成、算力增长、存算一体技术落地、先进封装等维度的未来发展趋势,以期为车载AI芯片技术研发、产品迭代、产业布局及行业相关研究工作提供参考与借鉴。
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