| Title | (Invited Paper) Machine Learning and Pattern Matching in Physical Design |
| Author | Bei Yu, *David Z. Pan (University of Texas at Austin, U.S.A.), Tetsuaki Matsunawa (Toshiba Corporation, Japan), Xuan Zeng (Fudan University, China) |
| Page | pp. 286 - 293 |
| Keyword | Machine Learning, Pattern Matching, Physical Design |
| Abstract | Machine learning (ML) and pattern matching (PM) are powerful computer science techniques which can derive knowledge from big data, and provide prediction and matching. Since nanometer VLSI design and manufacturing have extremely high complexity and gigantic data, there has been a surge recently in applying and adapting machine learning and pattern matching techniques in VLSI physical design (including physical verification), e.g., lithography hotspot detection and data/pattern-driven physical design, as ML and PM can raise the level of abstraction from detailed physics-based simulations and provide reasonably good quality-of-result. In this paper, we will discuss key techniques and recent results of machine learning and pattern matching, with their applications in physical design. |
| Title | (Invited Paper) Self-Learning and Adaptive Board-Level Functional Fault Diagnosis |
| Author | Fangming Ye, *Krishnendu Chakrabarty (Duke University, U.S.A.), Zhaobo Zhang, Xinli Gu (Huawei Technologies, U.S.A.) |
| Page | pp. 294 - 301 |
| Keyword | board-level, machine learning, fault diagnosis |
| Abstract | Functional fault diagnosis is necessary for board-level product qualification. However, ambiguous diagnosis results can lead to long debug times and wrong repair actions, which significantly increase repair cost and adversely impact yield. A state-of-the-art functional fault diagnosis system involves several key components: (1) design of functional test programs, (2) collection of functional-failure syndromes, (3) building of the diagnosis engine, (4) isolation of root causes, and (5) evaluation of the diagnosis engine. Advances in each of these components can pave the way for a more effective diagnosis system, thus improving diagnosis accuracy and reducing diagnosis time.Machine-learning and data analysis techniques offer an unprecedented opportunity to develop an automated and adaptive diagnosis system to increase diagnosis accuracy and reduce diagnosis time. This talk will describe how all the above components of an advanced diagnosis system can benefit from machine learning and information theory. Some of the topics to be discussed include incremental learning, decision trees, root-cause analysis and evaluation metrics, data acquisition, and knowledge transfer. |
| Title | (Invited Paper) Fast Statistical Analysis of Rare Failure Events for Memory Circuits in High-Dimensional Variation Space |
| Author | Shupeng Sun, *Xin Li (Carnegie Mellon University, U.S.A.) |
| Page | pp. 302 - 307 |
| Keyword | Memory, Monte Carlo |
| Abstract | Accurately estimating the rare failure rates for nanoscale memory circuits is a challenging task, especially when the variation space is high-dimensional. In this paper, we summarize two novel techniques to address this technical challenge. First, we describe a subset simulation (SUS) technique to estimate the rare failure rates for continuous performance metrics. The key idea of SUS is to express the rare failure probability of a given circuit as the product of several large conditional probabilities by introducing a number of intermediate failure events. These conditional probabilities can be efficiently estimated with a set of Markov chain Monte Carlo samples generated by a modified Metropolis algorithm. Second, to efficiently estimate the rare failure rates for discrete performance metrics, scaled-sigma sampling (SSS) can be used. SSS aims to generate random samples from a distorted probability distribution for which the standard deviation (i.e., sigma) is scaled up. Next, the failure rate is accurately estimated from these scaled random samples by using an analytical model derived from the theorem of “soft maximum”. Our experimental results of several nanoscale circuit examples demonstrate that SUS and SSS achieve significantly improved accuracy over other traditional techniques when the dimensionality of the variation space is more than a few hundred. |
| Slides | |
| Title | (Invited Paper) Data Mining in Functional Test Content Optimization |
| Author | *Li-C. Wang (University of California at Santa Barbara, U.S.A.) |
| Page | pp. 308 - 315 |
| Keyword | Functional verification, Data Mining, Test content optimization, Machine learning |
| Abstract | This paper reviews the data mining methodologies proposed for functional test content optimization where tests are sequences of instructions or transactions. Basic machine learning concepts and the key ideas of these methodologies are explained. Challenges for implementing these methodologies in practice are illustrated. Promises are demonstrated through experimental results based on industrial verification settings. |