Pub Date : 2024-11-06DOI: 10.1109/TCAD.2024.3443008
Hasini Witharana;Hansika Weerasena;Prabhat Mishra
Trusted execution environments (TEEs) provide a secure environment for computation, ensuring that the code and data inside the TEE are protected with respect to confidentiality and integrity. Virtual machine (VM)-based TEEs extend this concept by utilizing virtualization technology to create isolated execution spaces that can support a complete operating system or specific applications. As the complexity and importance of VM-based TEEs grow, ensuring their reliability and security through formal verification becomes crucial. However, these technologies often operate without formal assurances of their security properties. Our research introduces a formal framework for representing and verifying VM-based TEEs. This approach provides a rigorous foundation for defining and verifying key security attributes for safeguarding execution environments. To demonstrate the applicability of our verification framework, we conduct an analysis of real-world TEE platforms, including Intel’s trust domain extensions (TDX). This work not only emphasizes the necessity of formal verification in enhancing the security of VM-based TEEs but also provides a systematic approach for evaluating the resilience of these platforms against sophisticated adversarial models.
可信执行环境（TEE）为计算提供了一个安全的环境，确保 TEE 内的代码和数据在保密性和完整性方面受到保护。基于虚拟机（VM）的 TEE 扩展了这一概念，它利用虚拟化技术创建隔离的执行空间，可支持完整的操作系统或特定应用程序。随着基于虚拟机的 TEE 的复杂性和重要性不断增加，通过形式化验证确保其可靠性和安全性变得至关重要。然而，这些技术在运行时往往没有正式的安全属性保证。我们的研究为表示和验证基于虚拟机的 TEE 引入了一个形式框架。这种方法为定义和验证保障执行环境的关键安全属性奠定了坚实的基础。为了证明我们的验证框架的适用性，我们对现实世界中的 TEE 平台（包括英特尔的信任域扩展 (TDX)）进行了分析。这项工作不仅强调了形式化验证在增强基于虚拟机的 TEE 安全性方面的必要性，而且还提供了一种系统方法，用于评估这些平台对复杂对抗模型的适应能力。
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Pub Date : 2024-11-06DOI: 10.1109/TCAD.2024.3479791
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Pub Date : 2024-11-06DOI: 10.1109/TCAD.2024.3448299
Arshia Rafieioskouei;Borzoo Bonakdarpour
Causality is the relationship where one event contributes to the production of another, with the cause being partly responsible for the effect and the effect partly dependent on the cause. In this article, we propose a novel and effective method to formally reason about the causal effect of events in engineered systems, with application for finding the root-cause of safety violations in embedded and cyber-physical systems. We are motivated by the notion of actual causality by Halpern and Pearl, which focuses on the causal effect of particular events rather than type-level causality, which attempts to make general statements about scientific and natural phenomena. Our first contribution is formulating discovery of actual causality in computing systems modeled by transition systems as an satisfiability modulo theory solving problem. Since datasets for causality analysis tend to be large, in order to tackle the scalability problem of automated formal reasoning, our second contribution is a novel technique based on abstraction refinement that allows identifying for actual causes within smaller abstract causal models. We demonstrate the effectiveness of our approach (by several orders of magnitude) using three case studies to find the actual cause of violations of safety in 1) a neural network controller for a mountain car; 2) a controller for a Lunar Lander obtained by reinforcement learning; and 3) an MPC controller for an F-16 autopilot simulator.
因果关系是指一个事件促成另一个事件的产生，其中原因对结果负有部分责任，而结果则部分依赖于原因。在本文中，我们提出了一种新颖有效的方法，用于正式推理工程系统中事件的因果效应，并将其应用于查找嵌入式系统和网络物理系统中违反安全规定的根本原因。我们受 Halpern 和 Pearl 提出的实际因果关系概念的启发，该概念侧重于特定事件的因果效应，而不是类型级因果关系，后者试图对科学和自然现象做出一般性陈述。我们的第一个贡献是将在过渡系统建模的计算系统中发现实际因果关系表述为一个可满足性模态理论求解问题。由于因果关系分析的数据集往往很大，为了解决自动形式推理的可扩展性问题，我们的第二个贡献是基于抽象细化的新技术，它允许在较小的抽象因果模型中识别实际原因。我们通过三个案例研究证明了我们方法的有效性（提高了几个数量级），在以下三个方面找到了违反安全规定的实际原因：1）山地车的神经网络控制器；2）通过强化学习获得的月球着陆器控制器；3）F-16 自动驾驶模拟器的 MPC 控制器。
{"title":"Efficient Discovery of Actual Causality Using Abstraction Refinement","authors":"Arshia Rafieioskouei;Borzoo Bonakdarpour","doi":"10.1109/TCAD.2024.3448299","DOIUrl":"https://doi.org/10.1109/TCAD.2024.3448299","url":null,"abstract":"Causality is the relationship where one event contributes to the production of another, with the cause being partly responsible for the effect and the effect partly dependent on the cause. In this article, we propose a novel and effective method to formally reason about the causal effect of events in engineered systems, with application for finding the root-cause of safety violations in embedded and cyber-physical systems. We are motivated by the notion of actual causality by Halpern and Pearl, which focuses on the causal effect of particular events rather than type-level causality, which attempts to make general statements about scientific and natural phenomena. Our first contribution is formulating discovery of actual causality in computing systems modeled by transition systems as an satisfiability modulo theory solving problem. Since datasets for causality analysis tend to be large, in order to tackle the scalability problem of automated formal reasoning, our second contribution is a novel technique based on abstraction refinement that allows identifying for actual causes within smaller abstract causal models. We demonstrate the effectiveness of our approach (by several orders of magnitude) using three case studies to find the actual cause of violations of safety in 1) a neural network controller for a mountain car; 2) a controller for a Lunar Lander obtained by reinforcement learning; and 3) an MPC controller for an F-16 autopilot simulator.","PeriodicalId":13251,"journal":{"name":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","volume":"43 11","pages":"4274-4285"},"PeriodicalIF":2.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-06DOI: 10.1109/TCAD.2024.3443704
Masaki Waga;Étienne André
Hyperproperties enable simultaneous reasoning about multiple execution traces of a system and are useful to reason about noninterference, opacity, robustness, fairness, observational determinism, etc. We introduce hyper parametric timed computation tree logic (HyperPTCTL), extending hyperlogics with timing reasoning and, notably, parameters to express unknown values. We mainly consider its nest-free fragment, where the temporal operators cannot be nested. However, we allow extensions that enable counting actions and comparing the duration since the most recent occurrence of specific actions. We show that our nest-free fragment with this extension is sufficiently expressive to encode the properties, e.g., opacity, (un)fairness, or robust observational (non)determinism. We propose semi-algorithms for the model checking and synthesis of parametric timed automata (TAs) (an extension of TAs with timing parameters) against this nest-free fragment with the extension via reduction to the PTCTL model checking and synthesis. While the general model checking (and thus synthesis) problem is undecidable, we show that a large part of our extended (yet nest-free) fragment is decidable, provided the parameters only appear in the property, not in the model. We also exhibit additional decidable fragments where the parameters within the model are allowed. We implemented our semi-algorithms on the top of the IMITATOR model checker and performed experiments. Our implementation supports most of the nest-free fragments (beyond the decidable classes). The experimental results highlight our method’s practical relevance.
超属性可以同时推理系统的多个执行轨迹，对于推理不干涉、不透明、鲁棒性、公平性、观测确定性等非常有用。我们引入了超参数定时计算树逻辑（HyperPTCTL），用定时推理扩展了超逻辑，特别是用参数来表达未知值。我们主要考虑其无嵌套片段，其中的时序算子不能嵌套。不过，我们允许对其进行扩展，使其能够计算动作和比较特定动作最近一次发生后的持续时间。我们证明，我们的无嵌套片段与这种扩展具有足够的表现力，可以编码不透明、（不）公平或稳健观察（非）确定性等属性。我们提出了参数定时自动机（TAs）模型检查和合成的半算法（TAs 是带定时参数的 TAs 的扩展），通过还原为 PTCTL 模型检查和合成的方式，针对这个带扩展的无嵌套片段进行模型检查和合成。虽然一般的模型检查（以及合成）问题是不可解的，但我们证明，只要参数只出现在属性中，而不出现在模型中，我们扩展的（但无嵌套的）片段的很大一部分就是可解的。我们还展示了允许在模型中使用参数的其他可解片段。我们在 IMITATOR 模型检查器之上实现了我们的半算法，并进行了实验。我们的实现支持大多数无嵌套片段（除可判定类外）。实验结果凸显了我们方法的实用性。
{"title":"Hyper Parametric Timed CTL","authors":"Masaki Waga;Étienne André","doi":"10.1109/TCAD.2024.3443704","DOIUrl":"https://doi.org/10.1109/TCAD.2024.3443704","url":null,"abstract":"Hyperproperties enable simultaneous reasoning about multiple execution traces of a system and are useful to reason about noninterference, opacity, robustness, fairness, observational determinism, etc. We introduce hyper parametric timed computation tree logic (HyperPTCTL), extending hyperlogics with timing reasoning and, notably, parameters to express unknown values. We mainly consider its nest-free fragment, where the temporal operators cannot be nested. However, we allow extensions that enable counting actions and comparing the duration since the most recent occurrence of specific actions. We show that our nest-free fragment with this extension is sufficiently expressive to encode the properties, e.g., opacity, (un)fairness, or robust observational (non)determinism. We propose semi-algorithms for the model checking and synthesis of parametric timed automata (TAs) (an extension of TAs with timing parameters) against this nest-free fragment with the extension via reduction to the PTCTL model checking and synthesis. While the general model checking (and thus synthesis) problem is undecidable, we show that a large part of our extended (yet nest-free) fragment is decidable, provided the parameters only appear in the property, not in the model. We also exhibit additional decidable fragments where the parameters within the model are allowed. We implemented our semi-algorithms on the top of the IMITATOR model checker and performed experiments. Our implementation supports most of the nest-free fragments (beyond the decidable classes). The experimental results highlight our method’s practical relevance.","PeriodicalId":13251,"journal":{"name":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","volume":"43 11","pages":"4286-4297"},"PeriodicalIF":2.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-06DOI: 10.1109/TCAD.2024.3448306
P. Habeeb;Deepak D’Souza;Kamal Lodaya;Pavithra Prabhakar
We propose an abstraction-refinement-based algorithm for the problem of verifying the safety of a camera-based autonomous system in a synthetic 3D-scene, based on the notion of interval images. An interval image is an abstract data structure that represents a set of images in a 3D-scene. We give a computer graphics style rendering algorithm to efficiently compute interval images from a given region. Our proposed abstraction-refinement algorithm leverages recent abstract interpretation tools for neural networks. We have implemented and evaluated the proposed technique on complex 3D-scenes, demonstrating its effectiveness and scalability in comparison with earlier techniques.
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Pub Date : 2024-11-06DOI: 10.1109/TCAD.2024.3438072
Navid Hashemi;Lars Lindemann;Jyotirmoy V. Deshmukh
Reachability analysis is a popular method to give safety guarantees for stochastic cyber-physical systems (SCPSs) that takes in a symbolic description of the system dynamics and uses set-propagation methods to compute an overapproximation of the set of reachable states over a bounded time horizon. In this article, we investigate the problem of performing reachability analysis for an SCPS that does not have a symbolic description of the dynamics, but instead is described using a digital twin model that can be simulated to generate system trajectories. An important challenge is that the simulator implicitly models a probability distribution over the set of trajectories of the SCPS; however, it is typical to have a sim2real gap, i.e., the actual distribution of the trajectories in a deployment setting may be shifted from the distribution assumed by the simulator. We thus propose a statistical reachability analysis technique that, given a user-provided threshold $1-epsilon $ , provides a set that guarantees that any trajectory during deployment lies in this set with probability not smaller than this threshold. Our method is based on three main steps: 1) learning a deterministic surrogate model from sampled trajectories; 2) conducting reachability analysis over the surrogate model; and 3) employing robust conformal inference (CI) using an additional set of sampled trajectories to quantify the surrogate model’s distribution shift with respect to the deployed SCPS. To counter conservatism in reachable sets, we propose a novel method to train surrogate models that minimizes a quantile loss term (instead of the usual mean squared loss), and a new method that provides tighter guarantees using CI using a normalized surrogate error. We demonstrate the effectiveness of our technique on various case studies.
{"title":"Statistical Reachability Analysis of Stochastic Cyber-Physical Systems Under Distribution Shift","authors":"Navid Hashemi;Lars Lindemann;Jyotirmoy V. Deshmukh","doi":"10.1109/TCAD.2024.3438072","DOIUrl":"https://doi.org/10.1109/TCAD.2024.3438072","url":null,"abstract":"Reachability analysis is a popular method to give safety guarantees for stochastic cyber-physical systems (SCPSs) that takes in a symbolic description of the system dynamics and uses set-propagation methods to compute an overapproximation of the set of reachable states over a bounded time horizon. In this article, we investigate the problem of performing reachability analysis for an SCPS that does not have a symbolic description of the dynamics, but instead is described using a digital twin model that can be simulated to generate system trajectories. An important challenge is that the simulator implicitly models a probability distribution over the set of trajectories of the SCPS; however, it is typical to have a sim2real gap, i.e., the actual distribution of the trajectories in a deployment setting may be shifted from the distribution assumed by the simulator. We thus propose a statistical reachability analysis technique that, given a user-provided threshold \u0000<inline-formula> <tex-math>$1-epsilon $ </tex-math></inline-formula>\u0000, provides a set that guarantees that any trajectory during deployment lies in this set with probability not smaller than this threshold. Our method is based on three main steps: 1) learning a deterministic surrogate model from sampled trajectories; 2) conducting reachability analysis over the surrogate model; and 3) employing robust conformal inference (CI) using an additional set of sampled trajectories to quantify the surrogate model’s distribution shift with respect to the deployed SCPS. To counter conservatism in reachable sets, we propose a novel method to train surrogate models that minimizes a quantile loss term (instead of the usual mean squared loss), and a new method that provides tighter guarantees using CI using a normalized surrogate error. We demonstrate the effectiveness of our technique on various case studies.","PeriodicalId":13251,"journal":{"name":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","volume":"43 11","pages":"4250-4261"},"PeriodicalIF":2.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-06DOI: 10.1109/TCAD.2024.3445813
P. Habeeb;Lipsy Gupta;Pavithra Prabhakar
In this article, we consider the problem of checking approximate conformance of closed-loop systems with the same plant but different neural network (NN) controllers. First, we introduce a notion of approximate conformance on NNs, which allows us to quantify semantically the deviations in closed-loop system behaviors with different NN controllers. Next, we consider the problem of computationally checking this notion of approximate conformance on two NNs. We reduce this problem to that of reachability analysis on a combined NN, thereby, enabling the use of existing NN verification tools for conformance checking. Our experimental results on an autonomous rocket landing system demonstrate the feasibility of checking approximate conformance on different NNs trained for the same dynamics, as well as the practical semantic closeness exhibited by the corresponding closed-loop systems.
在本文中，我们考虑了检查具有相同工厂但不同神经网络 (NN) 控制器的闭环系统的近似一致性问题。首先，我们引入了神经网络近似一致性的概念，通过这一概念，我们可以从语义上量化不同神经网络控制器的闭环系统行为偏差。接下来，我们将考虑对两个 NN 的近似一致性概念进行计算检查的问题。我们将这一问题简化为对组合 NN 的可达性分析，从而使现有的 NN 验证工具能够用于一致性检查。我们在一个自主火箭着陆系统上的实验结果表明，在为相同动力学训练的不同 NN 上检查近似一致性是可行的，相应的闭环系统也表现出了实际的语义接近性。
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Exploring the design space of RISC-V processors faces significant challenges due to the vastness of the high-dimensional design space and the associated expensive simulation costs. This work proposes a region of interest (ROI)-driven method, which focuses on the promising ROIs to reduce the over-exploration on the huge design space and improve the optimization efficiency. A tree structure based on self-organizing map (SOM) networks is proposed to partition the design space into ROIs. To reduce the high dimensionality of design space, a variable selection technique based on a sensitivity matrix is developed to prune unimportant design parameters and efficiently hit the optimum inside the ROIs. Moreover, an asynchronous parallel strategy is employed to further save the time taken by simulations. Experimental results demonstrate the superiority of our proposed method, achieving improvements of up to 43.82% in performance, 33.20% in power consumption, and 11.41% in area compared to state-of-the-art methods.
{"title":"ROI-HIT: Region of Interest-Driven High-Dimensional Microarchitecture Design Space Exploration","authors":"Xuyang Zhao;Tianning Gao;Aidong Zhao;Zhaori Bi;Changhao Yan;Fan Yang;Sheng-Guo Wang;Dian Zhou;Xuan Zeng","doi":"10.1109/TCAD.2024.3443006","DOIUrl":"https://doi.org/10.1109/TCAD.2024.3443006","url":null,"abstract":"Exploring the design space of RISC-V processors faces significant challenges due to the vastness of the high-dimensional design space and the associated expensive simulation costs. This work proposes a region of interest (ROI)-driven method, which focuses on the promising ROIs to reduce the over-exploration on the huge design space and improve the optimization efficiency. A tree structure based on self-organizing map (SOM) networks is proposed to partition the design space into ROIs. To reduce the high dimensionality of design space, a variable selection technique based on a sensitivity matrix is developed to prune unimportant design parameters and efficiently hit the optimum inside the ROIs. Moreover, an asynchronous parallel strategy is employed to further save the time taken by simulations. Experimental results demonstrate the superiority of our proposed method, achieving improvements of up to 43.82% in performance, 33.20% in power consumption, and 11.41% in area compared to state-of-the-art methods.","PeriodicalId":13251,"journal":{"name":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","volume":"43 11","pages":"4178-4189"},"PeriodicalIF":2.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-06DOI: 10.1109/TCAD.2024.3445815
A. Alper Goksoy;Alish Kanani;Satrajit Chatterjee;Umit Ogras
Machine learning (ML) algorithms are being rapidly adopted to perform dynamic resource management tasks in heterogeneous system on chips. For example, ML-based task schedulers can make quick, high-quality decisions at runtime. Like any ML model, these offline-trained policies depend critically on the representative power of the training data. Hence, their performance may diminish or even catastrophically fail under unknown workloads, especially new applications. This article proposes a novel framework to continuously monitor the system to detect unforeseen scenarios using a gradient-based generalization metric called coherence. The proposed framework accurately determines whether the current policy generalizes to new inputs. If not, it incrementally trains the ML scheduler to ensure the robustness of the task-scheduling decisions. The proposed framework is evaluated thoroughly with a domain-specific SoC and six real-world applications. It can detect whether the trained scheduler generalizes to the current workload with 88.75%–98.39% accuracy. Furthermore, it enables $1.1times -14times $ faster execution time when the scheduler is incrementally trained. Finally, overhead analysis performed on an Nvidia Jetson Xavier NX board shows that the proposed framework can run as a real-time background task.
机器学习（ML）算法正被迅速用于执行异构片上系统中的动态资源管理任务。例如，基于 ML 的任务调度器可以在运行时做出快速、高质量的决策。与任何 ML 模型一样，这些离线训练的策略在很大程度上取决于训练数据的代表性。因此，在未知工作负载（尤其是新应用）下，它们的性能可能会下降，甚至出现灾难性故障。本文提出了一个新颖的框架，利用基于梯度的泛化指标--一致性，持续监控系统以检测不可预见的情况。所提出的框架能准确判断当前策略是否能泛化到新的输入。如果不能，它将逐步训练 ML 调度器，以确保任务调度决策的稳健性。我们利用特定领域的 SoC 和六个实际应用对所提出的框架进行了全面评估。它能以 88.75%-98.39% 的准确率检测出训练有素的调度程序是否适用于当前工作负载。此外，在对调度器进行增量训练时，它还能使执行时间缩短1.1倍-14倍。最后，在Nvidia Jetson Xavier NX板上进行的开销分析表明，所提出的框架可以作为实时后台任务运行。
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Neural networks have emerged as powerful tools across various domains, exhibiting remarkable empirical performance that motivated their widespread adoption in safety-critical applications, which, in turn, necessitates rigorous formal verification techniques to ensure their reliability and robustness. Tight bound propagation plays a crucial role in the formal verification process by providing precise bounds that can be used to formulate and verify properties, such as safety, robustness, and fairness. While state-of-the-art tools use linear and convex approximations to compute upper/lower bounds for each neuron’s outputs, recent advances have shown that nonlinear approximations based on Bernstein polynomials lead to tighter bounds but suffer from scalability issues. To that end, this article introduces BERN-NN-IBF, a significant enhancement of the Bernstein-polynomial-based bound propagation algorithms. BERN-NN-IBF offers three main contributions: 1) a memory-efficient encoding of Bernstein polynomials to scale the bound propagation algorithms; 2) optimized tensor operations for the new polynomial encoding to maintain the integrity of the bounds while enhancing computational efficiency; and 3) tighter under-approximations of the ReLU activation function using quadratic polynomials tailored to minimize approximation errors. Through comprehensive testing, we demonstrate that BERN-NN-IBF achieves tighter bounds and higher computational efficiency compared to the original BERN-NN and state-of-the-art methods, including linear and convex programming used within the winner of the VNN-COMPETITION.
{"title":"BERN-NN-IBF: Enhancing Neural Network Bound Propagation Through Implicit Bernstein Form and Optimized Tensor Operations","authors":"Wael Fatnassi;Arthur Feeney;Valen Yamamoto;Aparna Chandramowlishwaran;Yasser Shoukry","doi":"10.1109/TCAD.2024.3447577","DOIUrl":"https://doi.org/10.1109/TCAD.2024.3447577","url":null,"abstract":"Neural networks have emerged as powerful tools across various domains, exhibiting remarkable empirical performance that motivated their widespread adoption in safety-critical applications, which, in turn, necessitates rigorous formal verification techniques to ensure their reliability and robustness. Tight bound propagation plays a crucial role in the formal verification process by providing precise bounds that can be used to formulate and verify properties, such as safety, robustness, and fairness. While state-of-the-art tools use linear and convex approximations to compute upper/lower bounds for each neuron’s outputs, recent advances have shown that nonlinear approximations based on Bernstein polynomials lead to tighter bounds but suffer from scalability issues. To that end, this article introduces BERN-NN-IBF, a significant enhancement of the Bernstein-polynomial-based bound propagation algorithms. BERN-NN-IBF offers three main contributions: 1) a memory-efficient encoding of Bernstein polynomials to scale the bound propagation algorithms; 2) optimized tensor operations for the new polynomial encoding to maintain the integrity of the bounds while enhancing computational efficiency; and 3) tighter under-approximations of the ReLU activation function using quadratic polynomials tailored to minimize approximation errors. Through comprehensive testing, we demonstrate that BERN-NN-IBF achieves tighter bounds and higher computational efficiency compared to the original BERN-NN and state-of-the-art methods, including linear and convex programming used within the winner of the VNN-COMPETITION.","PeriodicalId":13251,"journal":{"name":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","volume":"43 11","pages":"4334-4345"},"PeriodicalIF":2.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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