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BitFREE: On Significant Speedup and Security Applications of FPGA Bitstream Format Reverse Engineering BitFREE: FPGA比特流格式逆向工程的显著加速和安全应用
Pub Date : 2023-05-22 DOI: 10.1109/ETS56758.2023.10174155
Zhang Tao, M. Tehranipoor, Farimah Farahmandi
FPGAs have been widely deployed in critical applications ranging from consumer electronics to spacecraft while the mainstream vendors refuse to disclose the details of their configuration bitstream format for security considerations but obstruct benign applications at the same time. Despite several bitstream reverse engineering solutions being proposed to reconstruct the bitstream formats, the state-of-the-art techniques typically require at least days to partially retrieve the architecture-specific bitstream format for a single (small) FPGA model. In this paper, we propose our BitFREE methodology which targets the most market-dominating Xilinx devices to reverse engineer the majority of bitstream formats of all models in different FPGA families at the time in the order of minutes by utilizing the correlation between FPGA architecture and the configuration memory map to decompose the configuration frames into more fine-grained segments for intelligent parallel analysis instead of directly analyzing entire bitstreams serially like other works. We demonstrate the high accuracy of BitFREE by recovering the information precisely from bitstreams of covered FPGA models. Also, we introduce two security applications of BitFREE, i.e., routing-level bitstream tampering and malicious ring oscillator circuitry detection, to shed light on the broad usage of bitstream reverse engineering in the hardware security domain.
fpga已经广泛应用于从消费电子到航天器的关键应用中,但主流供应商出于安全考虑拒绝透露其配置比特流格式的细节,同时也阻碍了良性应用。尽管提出了几种比特流逆向工程解决方案来重建比特流格式,但最先进的技术通常需要至少几天的时间来部分检索单个(小型)FPGA模型的特定架构的比特流格式。在本文中,我们提出了我们的BitFREE方法,该方法针对最占市场主导地位的Xilinx设备,通过利用FPGA架构和配置内存映射之间的相关性,将配置帧分解为更细粒度的片段进行智能并行分析,从而在几分钟内对不同FPGA家族中所有型号的大多数比特流格式进行逆向工程,而不是像其他作品那样直接分析整个比特流。我们通过精确地从覆盖的FPGA模型的比特流中恢复信息来证明BitFREE的高精度。此外,我们还介绍了BitFREE的两种安全应用,即路由级比特流篡改和恶意环振电路检测,以阐明比特流逆向工程在硬件安全领域的广泛应用。
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引用次数: 1
Physical-aware Interconnect Testing and Repairing of Chiplets 小芯片的物理感知互连测试与修复
Pub Date : 2023-05-22 DOI: 10.1109/ETS56758.2023.10174179
C. Cui, Tuanhui Xu, Haitao Fu, Junlin Huang
As the interconnect density of chiplets increases rapidly, some physics related defects appeared, such as coupling defects, etc. These defects are hard to detect with ordinary pseudo-random sequence patterns, some special test patterns are needed. Besides, the chip warpage caused by the thinning of 3D chips manufacturing and the uneven stress around TSVs or micro bumps will bring clustered faults of interconnections. For these defects, the repair rate of conventional interconnect redundancy method will be decreased. This paper proposes a physical-aware interconnect testing and repairing method of chiplets, using specific test patterns and clustered faults redundancy circuits to improve the interconnect test coverage and repair rate of chiplets. We also propose automatic repair circuits and the repair data synchronization scheme between multiple dies, so that the calculating and programming of repair data do not need to rely on the ATE programming, and the synchronization of the repair data between multiple dies can be done by hardware circuits automatically, which ensure the interconnection correctly after repairing.
随着晶片互连密度的快速增加,出现了一些物理缺陷,如耦合缺陷等。这些缺陷很难用普通的伪随机序列模式检测出来,需要一些特殊的测试模式。此外,由于三维芯片制造变薄导致的芯片翘曲以及tsv周围应力不均或微凸起会导致互连的聚集性故障。对于这些缺陷,传统互连冗余方法的修复率会降低。本文提出了一种基于物理感知的小芯片互连测试与修复方法,采用特定的测试模式和聚类故障冗余电路,提高了小芯片互连测试覆盖率和修复率。提出了自动修模电路和多模间修模数据同步方案,使修模数据的计算和编程不需要依赖于ATE编程,多模间修模数据的同步可以由硬件电路自动完成,保证修模后的正确互连。
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引用次数: 0
Semi-Supervised Deep Learning for Microcontroller Performance Screening 微控制器性能筛选的半监督深度学习
Pub Date : 2023-05-22 DOI: 10.1109/ETS56758.2023.10174083
N. Bellarmino, R. Cantoro, M. Huch, T. Kilian, Ulf Schlichtmann, Giovanni Squillero
In safety-critical applications, microcontrollers must satisfy strict quality constraints and performances in terms of Fmax (the maximum operating frequency). Data extracted from on-chip ring oscillators (ROs) can model the Fmax of integrated circuits using machine learning models. Those models are suitable for the performance screening process. Acquiring data from the ROs is a fast process that leads to many unlabeled data. Contrarily, the labeling phase (i.e., acquiring Fmax) is a time-consuming and costly task, that leads to a small set of labeled data. This paper presents deep-learning-based methodologies to cope with the low number of labeled data in microcontroller performance screening. We propose a method that takes advantage of the high number of unlabeled samples in a semi-supervised learning fashion. We derive deep feature extractor models that project data into higher dimensional spaces and use the data feature embedding to face the performance prediction problem with simple linear regression. Experiments showed that the proposed models outperformed state-of-the-art methodologies in terms of prediction error and permitted us to use a significantly smaller number of devices to be characterized, thus reducing the time needed to build ML models by a factor of six with respect to baseline approaches.
在安全关键应用中,微控制器必须满足严格的质量约束和Fmax(最大工作频率)方面的性能。从片上环形振荡器(ROs)中提取的数据可以使用机器学习模型对集成电路的Fmax进行建模。这些模型适用于性能筛选过程。从ro获取数据是一个快速的过程,会导致许多未标记的数据。相反,标记阶段(即获取Fmax)是一项耗时且昂贵的任务,这导致标记数据集很小。本文提出了基于深度学习的方法来处理微控制器性能筛选中标记数据数量少的问题。我们提出了一种半监督学习方式利用大量未标记样本的方法。我们推导了深度特征提取器模型,将数据投影到高维空间,并使用数据特征嵌入来解决简单线性回归的性能预测问题。实验表明,所提出的模型在预测误差方面优于最先进的方法,并允许我们使用显着较少数量的设备进行表征,从而将构建ML模型所需的时间减少了六倍。
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引用次数: 3
Data Background-Based Test Development for All Interconnect and Contact Defects in RRAMs rram中所有互连和接触缺陷的数据背景测试开发
Pub Date : 2023-05-22 DOI: 10.1109/ETS56758.2023.10174106
Hanzhi Xun, M. Fieback, S. Yuan, Ziwei Zhang, M. Taouil, S. Hamdioui
Resistive Random Access Memory (RRAM) is a potential technology to replace conventional memories by providing low power consumption and high-density storage. As various manufacturing vendors make significant efforts to push it to high-volume production and commercialization, high-quality and efficient test solutions are of great importance. This paper analyzes interconnect and contact defects in RRAMs, while considering the impact of the memory Data Background (DB), and proposes test solutions. The complete interconnect and contact defect space in a layout-independent RRAM design is defined. Exhaustive defect injection and circuit simulation are performed in a systematic manner to derive appropriate fault models, not only for single-cell and two-cell coupling faults, but also for multi-cell coupling faults where the DBs are important. The results show the existence of unique 3-cell and 4-cell coupling faults due to e.g., the sneak path in the array induced by defects. These unique faults cannot be detected with traditional RRAM test solutions. Therefore, the paper introduces a test generation method that takes into account the DB, which is able to efficiently detect all these faults; hence, further improving the fault/defect coverage in RRAMs.
电阻式随机存取存储器(RRAM)具有低功耗和高密度存储的特点,是一种有潜力取代传统存储器的技术。随着各种制造厂商努力将其推向大批量生产和商业化,高质量和高效的测试解决方案非常重要。分析了rram的互连和接触缺陷,同时考虑了内存数据背景(DB)的影响,并提出了测试解决方案。定义了与布局无关的RRAM设计中的完整互连和接触缺陷空间。穷尽缺陷注入和电路仿真以系统的方式推导出合适的故障模型,不仅适用于单单元和双单元耦合故障,而且适用于db很重要的多单元耦合故障。结果表明,由于缺陷引起的阵列内的潜行路径等原因,阵列存在独特的3-cell和4-cell耦合故障。这些独特的故障无法通过传统的RRAM测试解决方案检测到。因此,本文引入了一种考虑DB的测试生成方法,能够有效地检测出所有这些故障;因此,进一步提高rram中的故障/缺陷覆盖率。
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引用次数: 1
Online Fault-Tolerance for Memristive Neuromorphic Fabric Based on Local Approximation 基于局部逼近的记忆神经形态网络在线容错
Pub Date : 2023-05-22 DOI: 10.1109/ETS56758.2023.10174237
Soyed Tuhin Ahmed, R. Rakhmatullin, M. Tahoori
Neural networks (NNs) are a widely-used problem-solving tool, but their high computational and power consumption makes them expensive. Computation-in-Memory (CiM) architecture, which uses resistive non-volatile memories, is a promising solution due to its high energy efficiency. However, manufacturing defects and in-field faults can reduce the reliability and inference accuracy of CiM-implemented neural networks. Existing sophisticated fault detection and tolerance techniques require long downtime for testing and repair. In certain applications, e.g., "always on" NN applications, such downtime may not be acceptable. Thus, in this paper, a low-cost online fault tolerance technique based on local approximations is proposed to ensure continuous neural network operation with acceptable accuracy. Our approach reduces hardware overhead by up to 99.37% compared to conventional redundancy-based approaches while still achieving accuracy within 2% of the trained NNs.
神经网络(nn)是一种广泛使用的解决问题的工具,但其高计算和功耗使其昂贵。内存计算(CiM)架构使用电阻式非易失性存储器,由于其高能效而成为一种很有前途的解决方案。然而,制造缺陷和现场故障会降低基于cim的神经网络的可靠性和推理精度。现有复杂的故障检测和容限技术需要长时间的停机时间进行测试和修复。在某些应用中,例如,“始终在线”的神经网络应用,这样的停机时间可能是不可接受的。为此,本文提出了一种基于局部逼近的低成本在线容错技术,以保证神经网络以可接受的精度连续运行。与传统的基于冗余的方法相比,我们的方法减少了高达99.37%的硬件开销,同时仍然在训练好的神经网络的2%以内实现准确率。
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引用次数: 1
High Throughput and Energy Efficient SHA-2 ASIC Design for Continuous Integrity Checking Applications 用于持续完整性检查应用的高通量和节能SHA-2 ASIC设计
Pub Date : 2023-05-22 DOI: 10.1109/ETS56758.2023.10174095
Asimina Koutra, V. Tenentes
High throughput and energy efficient integrated cryptographic hash primitives are important for the continuous integrity checking and tampering detection in secure access management mechanisms of on-chip instrumentation, such as the IJTAG. However, previous SHA-256 cores focus only on throughput. In this paper, we synthesize with a 32 nm CMOS Technology SHA-256 cores that can be integrated in ASICs, and we present insights on their achieved throughput and energy efficiency. Moreover, we present a novel clock-gated design for reducing dynamic power dissipation of SHA-256 cores; and a novel Multi-Vt design for reducing static power dissipation of SHA-256 cores. The proposed designs can achieve upto 25.9% improvement of the energy efficiency of existing SHA-256 designs, without impacting their performed throughput. To the best of our knowledge, this is the first work that applies low power design techniques on SHA-256 cores.
高吞吐量和高能效的集成加密哈希原语对于片上仪器(如IJTAG)的安全访问管理机制中的连续完整性检查和篡改检测非常重要。然而,以前的SHA-256内核只关注吞吐量。在本文中,我们合成了可集成在asic中的32 nm CMOS技术SHA-256内核,并对其实现的吞吐量和能效提出了见解。此外,我们提出了一种新的时钟门控设计,以降低SHA-256内核的动态功耗;以及一种新颖的Multi-Vt设计,用于降低SHA-256内核的静态功耗。所提出的设计可以在不影响其执行吞吐量的情况下,将现有SHA-256设计的能源效率提高25.9%。据我们所知,这是第一个在SHA-256内核上应用低功耗设计技术的工作。
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引用次数: 0
Counterfeit Detection by Semiconductor Process Technology Inspection 半导体制程技术检测的防伪方法
Pub Date : 2023-05-22 DOI: 10.1109/ETS56758.2023.10174131
Matthias Ludwig, A. Bette, Bernhard Lippmann, G. Sigl
With world-wide distributed semiconductor supply chains and a scarcity of microelectronic products, counterfeit devices are gaining momentum. Sourcing products from trusted providers are the theoretical remedy, yet practice shows the reality. Forged electronics are entering the supply chain at a high rate and pose a threat to safety, reliability, and security. Academia and industry have established various pre- or post-production measures to effectively address this issue partially. Yet, several inadequately covered aspects of the field require improvements. First, this work introduces a rating scheme to enable the effective comparison between anti-counterfeiting methods. Recently published methods are compared using this scheme. Second, a novel, generic, generally applicable prover-verifier attestation framework for post-production anti-counterfeiting methods is established. Third, the work implements a new anti-counterfeit method. By introducing technological individual features, the method incorporates technology intrinsic features of the front-end semiconductor manufacturing process as technology distinctive characteristic. Profile parameters are extracted through pattern recognition and statistical methods which are compared to the expected technologies through distance metrics, allowing an assertion of device authenticity. Finally, the versatility of the method is experimentally validated through real samples. Overall, an accuracy of 100% is reported for seven samples which are checked for authenticity.
随着全球半导体供应链的分布和微电子产品的稀缺,假冒设备的势头正在增强。从可信的供应商那里采购产品是理论上的补救措施,但实践表明现实。伪造电子产品正以很高的速度进入供应链,并对安全性、可靠性和安全性构成威胁。学术界和工业界已经制定了各种制作前或制作后的措施来有效地解决这一问题。然而,该领域的几个未充分涵盖的方面需要改进。首先,本文引入了一种评级方案,使防伪方法之间能够进行有效的比较。使用该方案对最近发表的方法进行了比较。其次,建立了一种新的、通用的、普遍适用的证明者-验证者认证框架,用于后期防伪方法。第三,本工作实现了一种新的防伪方法。该方法通过引入技术个体特征,将半导体前端制造工艺的技术内在特征作为技术显著特征。通过模式识别和统计方法提取配置文件参数,通过距离度量将其与预期技术进行比较,从而可以断言设备的真实性。最后,通过实际样品验证了该方法的通用性。总体而言,对7个样品进行真实性检查,报告的准确性为100%。
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引用次数: 1
Intra-cell Resistive-Open Defect Analysis on a Foundry 8T SRAM-based IMC Architecture 晶圆厂8T sram基IMC架构的胞内阻开缺陷分析
Pub Date : 2023-05-22 DOI: 10.1109/ETS56758.2023.10174107
L. Ammoura, M. Flottes, P. Girard, J. Noel, A. Virazel
The adoption of In-Memory Computing (IMC) architectures is one of the promising approaches to efficiently solve the Von Neumann bottleneck problem. In addition to arithmetic operations, IMC architectures aim at integrating additional logic operations directly in the memory array or/and at the periphery for saving time and power consumption. In this paper, a comprehensive model of a 128x128 bitcell array based on a 28nm FD-SOI process technology has been considered to analyze the behavior of IMC 8T SRAM bitcells in the presence of resistive-open defects injected in the read port. A hierarchical analysis including a detailed study of each defect was performed in order to determine their impact both in memory and computing modes, both locally on the defective bitcell and globally on the array. Experimental results show that the IMC mode offers the most effective detectability of resistive-open defects.
采用内存计算(IMC)架构是有效解决冯诺依曼瓶颈问题的一种有前途的方法。除了算术运算之外,IMC架构的目标是直接在存储器阵列或/和外围集成额外的逻辑运算,以节省时间和功耗。本文考虑了基于28nm FD-SOI工艺技术的128x128位元阵列的综合模型,以分析IMC 8T SRAM位元在读端口注入电阻性开放缺陷时的行为。为了确定它们对内存和计算模式的影响,以及对局部有缺陷的位单元和全局阵列的影响,进行了分层分析,包括对每个缺陷的详细研究。实验结果表明,IMC模式对阻性开孔缺陷具有最有效的检测效果。
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引用次数: 0
ETS 2022 Best Paper ETS 2022年最佳论文
Pub Date : 2023-05-22 DOI: 10.1109/ets56758.2023.10174187
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引用次数: 0
SiCBit-PUF: Strong in-Cache Bitflip PUF Computation for Trusted SoCs SiCBit-PUF:用于可信soc的强缓存内位翻转PUF计算
Pub Date : 2023-05-22 DOI: 10.1109/ETS56758.2023.10173941
Athanasios Xynos, V. Tenentes, Y. Tsiatouhas
Secure computing necessitates hardware root of trust (RoT) integrated in Systems-on-Chips (SoCs) for cryptographic keys generation, authentication and identification. In this paper, we observe that bitflips in SRAM cells that appear while accessing multiple cells from the same bitline, are not stochastic, as previously considered, but systematic. Based on this observation, a novel strong in-memory Physical Unclonable Function (PUF) computation is proposed for harvesting static entropy from SRAM arrays. The proposed design is compatible with existing in-SRAM computing architectures. To verify our PUF operation, we implement a 6T SRAM array model that performs in-memory computing using a 32 nm CMOS Technology, and, through SPICE simulation, we evaluate the proposed PUF performance. The proposed PUF operation achieves uniqueness and uniformity of 49.99%, and 49.74%, respectively, and reliability higher than 97.4% when the temperature is varied from 0°C to 100°C, and higher than 95.2% when the nominal voltage supply is varied by 10%. Furthermore, we explore the scaling of the number of Challenge Response Pairs (CRPs) of the proposed PUF, and we compare it against the state-of-the-art. Our PUF offers orders of magnitude higher number of CRPs, therefore it is suitable for integrated mechanisms that assure secure computing in SoCs.
安全计算需要将硬件信任根(RoT)集成到片上系统(soc)中,用于加密密钥生成、认证和识别。在本文中,我们观察到,当从同一位线访问多个单元时出现的SRAM单元中的位翻转不是随机的,而是系统的。基于这一观察,提出了一种新的强内存物理不可克隆函数(PUF)计算方法,用于从SRAM阵列中获取静态熵。所提出的设计与现有的in-SRAM计算架构兼容。为了验证我们的PUF操作,我们实现了一个使用32纳米CMOS技术执行内存计算的6T SRAM阵列模型,并通过SPICE模拟评估了我们提出的PUF性能。所提出的PUF运行的唯一性和均匀性分别达到49.99%和49.74%,当温度在0 ~ 100℃范围内变化时,可靠性高于97.4%,当标称电压电源变化10%时,可靠性高于95.2%。此外,我们探讨了提出的PUF的挑战响应对(CRPs)数量的缩放,并将其与最先进的技术进行了比较。我们的PUF提供了更高数量级的crp数量,因此它适用于确保soc中安全计算的集成机制。
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引用次数: 0
期刊
2023 IEEE European Test Symposium (ETS)
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