David Z. Pan

Bio: David Z. Pan is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Routing (electronic design automation) & Computer science. The author has an hindex of 50, co-authored 496 publications receiving 10182 citations. Previous affiliations of David Z. Pan include University of California, Los Angeles & Fudan University.

AppSAT: Approximately deobfuscating integrated circuits

Abstract: In today's diversified semiconductor supply-chain, protecting intellectual property (IP) and maintaining manufacturing integrity are important concerns. Circuit obfuscation techniques such as logic encryption and IC camouflaging can potentially defend against a majority of supply-chain threats such as stealthy malicious design modification, IP theft, overproduction, and cloning. Recently, a Boolean Satisfiability (SAT) based attack, namely the SAT attack has been able to deobfuscate almost all traditional circuit obfuscation schemes, and as a result, a number of defense solutions have been proposed in literature. All these defenses are based on the implicit assumption that the attacker needs a perfect deobfuscation accuracy which may not be true in many practical cases. Therefore, in this paper by relaxing the exactness constraint on deobfuscation, we propose the AppSAT attack, an approximate deobfuscation algorithm based on the SAT attack and random testing. We show how the AppSAT attack can deobfuscate 68 out of the 71 benchmark circuits that were obfuscated with state-of-the-art SAT attack defenses with an accuracy of, n being the number of inputs. AppSAT shows that with current SAT attack defenses there will be a trade-off between exact-attack resiliency and approximation resiliency.

Layout decomposition for triple patterning lithography

Abstract: As minimum feature size and pitch spacing further decrease, triple patterning lithography (TPL) is a possible 193nm extension along the paradigm of double patterning lithography (DPL). However, there is very little study on TPL layout decomposition. In this paper, we show that TPL layout decomposition is a more difficult problem than that for DPL. We then propose a general integer linear programming formulation for TPL layout decomposition which can simultaneously minimize conflict and stitch numbers. Since ILP has very poor scalability, we propose three acceleration techniques without sacrificing solution quality: independent component computation, layout graph simplification, and bridge computation. For very dense layouts, even with these speedup techniques, ILP formulation may still be too slow. Therefore, we propose a novel vector programming formulation for TPL decomposition, and solve it through effective semidefinite programming (SDP) approximation. Experimental results show that the ILP with acceleration techniques can reduce 82% runtime compared to the baseline ILP. Using SDP based algorithm, the runtime can be further reduced by 42% with some tradeoff in the stitch number (reduced by 7%) and the conflict (9% more). However, for very dense layouts, SDP based algorithm can achieve 140× speed-up even compared with accelerated ILP.

Buffer block planning for interconnect-driven floorplanning

Abstract: This paper studies buffer block planning for interconnect-driven floorplanning in deep submicron designs. We first introduce the concept of feasible region (FR) for buffer insertion, and derive closed-form formula for FR. We observe that the FR for a buffer is quite large in general even under fairly tight delay constraint. Therefore, FR gives us a lot of flexibility to plan for buffer locations. We then develop an effective buffer block planning (BBP) algorithm to perform buffer clustering such that the overall chip area and the buffer block number can be minimized. To the best of our knowledge, this is the first in-depth study on buffer planning for interconnect-driven floorplanning with both area and delay consideration.

Pushing ASIC performance in a power envelope

Abstract: Power dissipation is becoming the most challenging design constraint in nanometer technologies. Among various design implementation schemes, standard cell ASICs offer the best power efficiency for high-performance applications. The flexibility of ASICs allow for the use of multiple voltages and multiple thresholds to match the performance of critical regions to their timing constraints, and minimize the power everywhere else. We explore the trade-off between multiple supply voltages and multiple threshold voltages in the optimization of dynamic and static power. The use of multiple supply voltages presents some unique physical and electrical challenges. Level shifters need to be introduced between the various voltage regions. Several level shifter implementations will be shown. The physical layout needs to be designed to ensure the efficient delivery of the correct voltage to various voltage regions. More flexibility can be gained by using appropriate level shifters. We will discuss optimization techniques such as clock skew scheduling which can be effectively used to push performance in a power neutral way.

Redundant-via enhanced maze routing for yield improvement

Abstract: Redundant via insertion is a good solution to reduce the yield loss by via failure. However, the existing methods are all post-layout optimizations that insert redundant via after detailed routing. In this paper, we propose the first routing algorithm that considers feasibility of redundant via insertion in the detailed routing stage. Our routing problem is formulated as maze routing with redundant via constraints. The problem is transformed to a multiple constraint shortest path problem, and solved by Lagrangian relaxation technique. Experimental results show that our algorithm can find routing layout with much higher rate of redundant via than conventional maze routing.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Integrative analysis of 111 reference human epigenomes

Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.