Adaptive Integrated Circuit Design for Variation Resilience and Security

The past few decades witness the burgeoning development of integrated circuit in terms of process technology scaling. Along with the tremendous benefits coming from the scaling, challenges are also presented in various stages. During the design time, the complexity of developing a circuit with millions to billions of smaller size transistors is extended after the variations are taken into account. The difficulty of analyzing these nondeterministic properties makes the allocation scheme of redundant resource hardly work in a cost-efficient way. Besides fabrication variations, analog circuits are suffered from severe performance degradations owing to their physical attributes which are vulnerable to aging effects. As such, the post-silicon calibration approach gains increasing attentions to compensate the performance mismatch. For the user-end applications, additional system failures result from the pirated and counterfeited devices provided by the untrusted semiconductor supply chain. Again analog circuits show their weakness to this threat due to the shortage of piracy avoidance techniques. In this dissertation, we propose three adaptive integrated circuit designs to overcome these challenges respectively. The first one investigates the variability-aware gate implementation with the consideration of the overhead control of adaptivity assignment. This design improves the variation resilience typically for digital circuits while optimizing the power consumption and timing yield. The second design is implemented as a self-validation system for the calibration of diverse analog circuits. The system is completely integrated on chip to enhance the convenience without external assistance. In the last design, a classic analog component is further studied to establish the configurable locking mechanism for analog circuits. The use of Satisfiability Modulo Theories addresses the difficulty of searching the unique unlocking pattern of non-Boolean variables

Automated Deduction – CADE 28

This open access book constitutes the proceeding of the 28th International Conference on Automated Deduction, CADE 28, held virtually in July 2021. The 29 full papers and 7 system descriptions presented together with 2 invited papers were carefully reviewed and selected from 76 submissions. CADE is the major forum for the presentation of research in all aspects of automated deduction, including foundations, applications, implementations, and practical experience. The papers are organized in the following topics: Logical foundations; theory and principles; implementation and application; ATP and AI; and system descriptions

Methoden und Beschreibungssprachen zur Modellierung und Verifikation vonSchaltungen und Systemen: MBMV 2015 - Tagungsband, Chemnitz, 03. - 04. März 2015

Der Workshop Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen (MBMV 2015) findet nun schon zum 18. mal statt. Ausrichter sind in diesem Jahr die Professur Schaltkreis- und Systementwurf der Technischen Universität Chemnitz und das Steinbeis-Forschungszentrum Systementwurf und Test. Der Workshop hat es sich zum Ziel gesetzt, neueste Trends, Ergebnisse und aktuelle Probleme auf dem Gebiet der Methoden zur Modellierung und Verifikation sowie der Beschreibungssprachen digitaler, analoger und Mixed-Signal-Schaltungen zu diskutieren. Er soll somit ein Forum zum Ideenaustausch sein. Weiterhin bietet der Workshop eine Plattform für den Austausch zwischen Forschung und Industrie sowie zur Pflege bestehender und zur Knüpfung neuer Kontakte. Jungen Wissenschaftlern erlaubt er, ihre Ideen und Ansätze einem breiten Publikum aus Wissenschaft und Wirtschaft zu präsentieren und im Rahmen der Veranstaltung auch fundiert zu diskutieren. Sein langjähriges Bestehen hat ihn zu einer festen Größe in vielen Veranstaltungskalendern gemacht. Traditionell sind auch die Treffen der ITGFachgruppen an den Workshop angegliedert. In diesem Jahr nutzen zwei im Rahmen der InnoProfile-Transfer-Initiative durch das Bundesministerium für Bildung und Forschung geförderte Projekte den Workshop, um in zwei eigenen Tracks ihre Forschungsergebnisse einem breiten Publikum zu präsentieren. Vertreter der Projekte Generische Plattform für Systemzuverlässigkeit und Verifikation (GPZV) und GINKO - Generische Infrastruktur zur nahtlosen energetischen Kopplung von Elektrofahrzeugen stellen Teile ihrer gegenwärtigen Arbeiten vor. Dies bereichert denWorkshop durch zusätzliche Themenschwerpunkte und bietet eine wertvolle Ergänzung zu den Beiträgen der Autoren. [... aus dem Vorwort

물리적 설계 자동화에서 표준셀 합성 및 최적화와 설계 품질 예측 방법론

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2023. 2. 김태환.In the physical design of chip implementation, designing high-quality standard cell layout and accurately predicting post-route DRV (design rule violation) at an early stage is an important problem, especially in advanced technology nodes. This dissertation presents two methodologies that can contribute to improving the design quality and design turnaround time of physical design flow. Firstly, we propose an integrated approach to the two problems of transistor folding and placement in standard cell layout synthesis. Precisely, we propose a globally optimal algorithm of search tree based design space exploration, devising a set of effective speeding up techniques as well as dynamic programming based fast cost computation. In addition, our algorithm incorporates the minimum oxide diffusion jog constraint, which closely relies on both of transistor folding and placement. Through experiments with the transistor netlists and design rules in advanced node, our proposed method is able to synthesize fully routable cell layouts of minimal size within a very fast time for each netlist, outperforming the cell layout quality in the manual design. Secondly, we propose a novel ML based DRC hotspot prediction technique, which is able to accurately capture the combined impact of pin accessibility and routing congestion on DRC hotspots. Precisely, we devise a graph, called pin proximity graph, that effectively models the spatial information on cell I/O pins and the information on pin-to-pin disturbance relation. Then, we propose a new ML model, which tightly combines GNN (graph neural network) and U-net in a way that GNN is used to embed pin accessibility information abstracted from our pin proximity graph while U-net is used to extract routing congestion information from grid-based features. Through experiments with a set of benchmark designs using advanced node, our model outperforms the existing ML models on all benchmark designs within the fast inference time in comparison with that of the state-of-the-art techniques.칩 구현의 물리적 설계 단계에서, 높은 성능의 표준 셀 설계와 배선 연결 이후 조기에 설계 규칙 위반을 정확히 예측하는 것은 최신 공정에서 특히 중요한 문제이다. 본 논문에서는 물리적 설계에서의 설계 품질과 총 설계 시간 향상을 달성할 수 있는 두 가지 방법론을 제안한다. 먼저, 본 논문에서는 표준 셀 레이아웃 합성에서 트랜지스터 폴딩과 배치를 종합적으로 진행할 수 있는 방법론을 논한다. 구체적으로 탐색 트리 기반의 최적화 알고리즘과 동적 프로그래밍 기반 빠른 비용 계산 방법과 여러 속도 개선 기법을 제안한다. 여기에 더해, 최신 공정에서 트랜지스터 폴딩과 배치로 인해 발생할 수 있는 최소 산화물 확산 영역 설계 규칙을 고려하였다. 최신 공정에 대한 표준 셀 합성 실험 결과, 본 논문에서 제안한 방법이 설계 전문가가 수동으로 설계한 것 대비 높은 성능을 보이고, 설계 시간도 매우 짧음을 보인다. 두번째로, 본 논문에서는 셀 배치 단계에서 핀 접근성과 연결 혼잡으로 인한 영향을 종합적으로 고려할 수 있는 머신 러닝 기반 설계 규칙 위반 구역 예측 방법론을 제안한다. 먼저 표준 셀의 입/출력 핀의 물리적 정보와 핀과 핀 사이 방해 관계를 효과적으로 표현할 수 있는 핀 근접 그래프를 제안하고, 그래프 신경망과 유넷 신경망을 효과적으로 결합한 새로운 형태의 머신 러닝 모델을 제안한다. 이 모델에서 그래프 신경망은 핀 근접 그래프로부터 핀 접근성 정보를 추출하고, 유넷 신경망은 격자 기반 특징으로부터 연결 혼잡 정보를 추출한다. 실험 결과 본 논문에서 제안한 방법은 이전 연구들 대비 더 빠른 예측 시간에 더 높은 예측 성능을 달성함을 보인다.1 Introduction 1 1.1 Standard Cell Layout Synthesis 1 1.2 Machine Learning for Electronic Design Automation 6 1.3 Prediction of Design Rule Violation 8 1.4 Contributions of This Dissertation 11 2 Standard Cell Layout Synthesis of Advanced Nodes with Simultaneous Transistor Folding and Placement 14 2.1 Motivations 14 2.2 Algorithm for Standard Cell Layout Synthesis 16 2.2.1 Problem Definition 16 2.2.2 Overall Flow 18 2.2.3 Step 1: Generation of Folding Shapes 18 2.2.4 Step 2: Search-tree Based Design Space Exploration 20 2.2.5 Speeding up Techniques 23 2.2.6 In-cell Routability Estimation 28 2.2.7 Step 3: In-cell Routing 30 2.2.8 Step 4: Splitting Folding Shapes 35 2.2.9 Step 5: Relaxing Minimum-area Constraints 37 2.3 Experimental Results 38 2.3.1 Comparison with ASAP 7nm Cell Layouts 40 2.3.2 Effectiveness of Dynamic Folding 42 2.3.3 Effectiveness of Speeding Up Techniques 43 2.3.4 Impact of Splitting Folding Shape 48 2.3.5 Runtime Analysis According to Area Relaxation 51 2.3.6 Comparison with Previous Works 52 3 Pin Accessibility and Routing Congestion Aware DRC Hotspot Prediction using Graph Neural Network and U-Net 54 3.1 Preliminary 54 3.1.1 Graph Neural Network 54 3.1.2 Fully Convolutional Network 56 3.2 Proposed Prediction Methodology 57 3.2.1 Overall Flow 57 3.2.2 Pin Proximity Graph 58 3.2.3 Grid-based Features 61 3.2.4 Overall Architecture of PGNN 64 3.2.5 GNN Architecture in PGNN 64 3.2.6 U-net Architecture in PGNN 66 3.2.7 Final Prediction in PGNN 66 3.3 Experimental Results 68 3.3.1 Experimental Setup 68 3.3.2 Analysis on PGNN Performance 71 3.3.3 Comparison with Previous Works 72 3.3.4 Adaptation to Real-world Designs 81 3.3.5 Handling Data Imbalance Problem in Regression Model 86 4 Conclusions 92 4.1 Chapter 2 92 4.2 Chapter 3 93박

Computer Aided Verification

The open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency

Algorithms for Verification of Analog and Mixed-Signal Integrated Circuits

Over the past few decades, the tremendous growth in the complexity of analog and mixed-signal (AMS) systems has posed great challenges to AMS verification, resulting in a rapidly growing verification gap. Existing formal methods provide appealing completeness and reliability, yet they suffer from their limited efficiency and scalability. Data oriented machine learning based methods offer efficient and scalable solutions but do not guarantee completeness or full coverage. Additionally, the trend towards shorter time to market for AMS chips urges the development of efficient verification algorithms to accelerate with the joint design and testing phases. This dissertation envisions a hierarchical and hybrid AMS verification framework by consolidating assorted algorithms to embrace efficiency, scalability and completeness in a statistical sense. Leveraging diverse advantages from various verification techniques, this dissertation develops algorithms in different categories. In the context of formal methods, this dissertation proposes a generic and comprehensive model abstraction paradigm to model AMS content with a unifying analog representation. Moreover, an algorithm is proposed to parallelize reachability analysis by decomposing AMS systems into subsystems with lower complexity, and dividing the circuit's reachable state space exploration, which is formulated as a satisfiability problem, into subproblems with a reduced number of constraints. The proposed modeling method and the hierarchical parallelization enhance the efficiency and scalability of reachability analysis for AMS verification. On the subject of learning based method, the dissertation proposes to convert the verification problem into a binary classification problem solved using support vector machine (SVM) based learning algorithms. To reduce the need of simulations for training sample collection, an active learning strategy based on probabilistic version space reduction is proposed to perform adaptive sampling. An expansion of the active learning strategy for the purpose of conservative prediction is leveraged to minimize the occurrence of false negatives. Moreover, another learning based method is proposed to characterize AMS systems with a sparse Bayesian learning regression model. An implicit feature weighting mechanism based on the kernel method is embedded in the Bayesian learning model for concurrent quantification of influence of circuit parameters on the targeted specification, which can be efficiently solved in an iterative method similar to the expectation maximization (EM) algorithm. Besides, the achieved sparse parameter weighting offers favorable assistance to design analysis and test optimization

Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

Quantum Circuit Reconstruction from Power Side-Channel Attacks on Quantum Computer Controllers

The interest in quantum computing has grown rapidly in recent years, and with it grows the importance of securing quantum circuits. A novel type of threat to quantum circuits that dedicated attackers could launch are power trace attacks. To address this threat, this paper presents first formalization and demonstration of using power traces to unlock and steal quantum circuit secrets. With access to power traces, attackers can recover information about the control pulses sent to quantum computers. From the control pulses, the gate level description of the circuits, and eventually the secret algorithms can be reverse engineered. This work demonstrates how and what information could be recovered. This work uses algebraic reconstruction from power traces to realize two new types of single trace attacks: per-channel and total power attacks. The former attack relies on per-channel measurements to perform a brute-force attack to reconstruct the quantum circuits. The latter attack performs a single-trace attack using Mixed-Integer Linear Programming optimization. Through the use of algebraic reconstruction, this work demonstrates that quantum circuit secrets can be stolen with high accuracy. Evaluation on 32 real benchmark quantum circuits shows that our technique is highly effective at reconstructing quantum circuits. The findings not only show the veracity of the potential attacks, but also the need to develop new means to protect quantum circuits from power trace attacks. Throughout this work real control pulse information from real quantum computers is used to demonstrate potential attacks based on simulation of collection of power traces