CSR: Small: Accelerating Microprocessor Post-Silicon Diagnosis with Statistical Inference

CSR：小：通过统计推断加速微处理器硅后诊断

• 批准号: 1217764
• 负责人: Valeria Bertacco
• 金额: $ 47.82万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217764&HistoricalAwards=false
• 关键词: CSR; Small; Accelerating; Microprocessor; Post

Finding bugs in modern complex microprocessors is a critical and daunting task that must be deftly mastered to move designs from first silicon to launch. Escaped bugs may lead to the demise of a digital silicon company. At the same time, looking for design errors on first silicon prototypes lacks the observability, controllability and repeatability afforded by pre-silicon simulation frameworks. To complicate the process further, many of the bugs that manifest in real silicon are the result of complex asynchronous interactions and/or electrical anomalies that are often not easily or frequently repeatable. Because of these challenges, debugging these "fleeting bugs" in early silicon is a black art that can significantly impact design schedules if the debugging process does not proceed smoothly.This project investigates solutions to support the efficient diagnosis of these most challenging post-silicon validation bugs, those that manifest only occasionally. These bugs may be functional, timing or electrical errors, or also missed manufacturing defects. The approach entails placing lightweight instrumentation on-chip to collect data during a prototype's test executions. The data is then analyzed offline using statistical inference algorithms to quickly point verification engineers to offending components. The research explores a range of ideas to find the most promising instrumentation and algorithms for analysis. The results of this research effort allow semiconductor companies to shorten their time to market while delivering high quality products, with low incidence of escaped bugs; in turn, the work benefits society in that it unlocks further scaling and growth for the electronics industry.

发现现代复杂微处理器中的错误是一项关键而艰巨的任务，必须熟练掌握这项任务，才能将设计从第一个硅片推向市场。逃脱的虫子可能会导致一家数字硅公司的灭亡。同时，在第一个硅原型上寻找设计错误缺乏前硅模拟框架所提供的可观察性、可控性和重复性。使这一过程进一步复杂化的是，在真实硅中表现出的许多错误是复杂的异步相互作用和/或电异常的结果，这些异常通常不容易或不频繁地重复。由于这些挑战，在早期硅中调试这些“转瞬即逝的错误”是一门黑术，如果调试过程进行得不顺利，可能会严重影响设计进度。本项目研究解决方案，以支持对这些最具挑战性的硅后验证错误的有效诊断，这些错误只是偶尔出现。这些错误可能是功能错误、定时错误或电气错误，也可能是遗漏的制造缺陷。这种方法需要在芯片上放置轻量级仪器，以在原型的测试执行期间收集数据。然后使用统计推断算法对数据进行离线分析，以快速将验证工程师指向违规组件。这项研究探索了一系列想法，以找到最有希望的分析仪器和算法。这项研究的结果使半导体公司在提供高质量产品的同时缩短了上市时间，并降低了逃逸错误的发生率；反过来，这项工作对社会有利，因为它为电子行业带来了进一步的规模和增长。