241 research outputs found

    On the Reuse of RTL assertions in Systemc TLM Verification

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    Reuse of existing and already verified intellectual property (IP) models is a key strategy to cope with the com- plexity of designing modern system-on-chips (SoC)s under ever stringent time-to-market requirements. In particular, the recent trend towards system-level design and transaction level modeling (TLM) gives rise to new challenges for reusing existing RTL IPs and their verification environment in TLM-based design flows. While techniques and tools to abstract RTL IPs into TLM models have begun to appear, the problem of reusing, at TLM, a verification environment originally developed for an RTL IP is still underexplored, particularly when assertion-based verification (ABV) is adopted. Some techniques and frameworks have been proposed to deal with ABV at TLM, but they assume a top-down design and verification flow, where assertions are defined ex-novo at TLM level. In contrast, the reuse of existing assertions in an RTL-to-TLM bottom-up design flow has not been analyzed yet. This paper proposes a methodology to reuse assertions originally defined for a given RTL IP, to verify the corresponding TLM model. Experimental results have been conducted on benchmarks of different characteristics and complexity to show the applicability and the efficacy of the proposed methodology

    Reusing RTL assertion checkers for verification of SystemC TLM models

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    The recent trend towards system-level design gives rise to new challenges for reusing existing RTL intellectual properties (IPs) and their verification environment in TLM. While techniques and tools to abstract RTL IPs into TLM models have begun to appear, the problem of reusing, at TLM, a verification environment originally developed for an RTL IP is still under-explored, particularly when ABV is adopted. Some frameworks have been proposed to deal with ABV at TLM, but they assume a top-down design and verification flow, where assertions are defined ex-novo at TLM level. In contrast, the reuse of existing assertions in an RTL-to-TLM bottom-up design flow has not been analyzed yet, except by using transactors to create a mixed simulation between the TLM design and the RTL checkers corresponding to the assertions. However, the use of transactors may lead to longer verification time due to the need of developing and verifying the transactors themselves. Moreover, the simulation time is negatively affected by the presence of transactors, which slow down the simulation at the speed of the slowest parts (i.e., RTL checkers). This article proposes an alternative methodology that does not require transactors for reusing assertions, originally defined for a given RTL IP, in order to verify the corresponding TLM model. Experimental results have been conducted on benchmarks with different characteristics and complexity to show the applicability and the efficacy of the proposed methodology

    Survey on Machine Learning Algorithms Enhancing the Functional Verification Process

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    The continuing increase in functional requirements of modern hardware designs means the traditional functional verification process becomes inefficient in meeting the time-to-market goal with sufficient level of confidence in the design. Therefore, the need for enhancing the process is evident. Machine learning (ML) models proved to be valuable for automating major parts of the process, which have typically occupied the bandwidth of engineers; diverting them from adding new coverage metrics to make the designs more robust. Current research of deploying different (ML) models prove to be promising in areas such as stimulus constraining, test generation, coverage collection and bug detection and localization. An example of deploying artificial neural network (ANN) in test generation shows 24.5× speed up in functionally verifying a dual-core RISC processor specification. Another study demonstrates how k-means clustering can reduce redundancy of simulation trace dump of an AHB-to-WHISHBONE bridge by 21%, thus reducing the debugging effort by not having to inspect unnecessary waveforms. The surveyed work demonstrates a comprehensive overview of current (ML) models enhancing the functional verification process from which an insight of promising future research areas is inferred

    LLM for SoC Security: A Paradigm Shift

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    As the ubiquity and complexity of system-on-chip (SoC) designs increase across electronic devices, the task of incorporating security into an SoC design flow poses significant challenges. Existing security solutions are inadequate to provide effective verification of modern SoC designs due to their limitations in scalability, comprehensiveness, and adaptability. On the other hand, Large Language Models (LLMs) are celebrated for their remarkable success in natural language understanding, advanced reasoning, and program synthesis tasks. Recognizing an opportunity, our research delves into leveraging the emergent capabilities of Generative Pre-trained Transformers (GPTs) to address the existing gaps in SoC security, aiming for a more efficient, scalable, and adaptable methodology. By integrating LLMs into the SoC security verification paradigm, we open a new frontier of possibilities and challenges to ensure the security of increasingly complex SoCs. This paper offers an in-depth analysis of existing works, showcases practical case studies, demonstrates comprehensive experiments, and provides useful promoting guidelines. We also present the achievements, prospects, and challenges of employing LLM in different SoC security verification tasks.Comment: 42 page

    Towards Multidimensional Verification: Where Functional Meets Non-Functional

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    Trends in advanced electronic systems' design have a notable impact on design verification technologies. The recent paradigms of Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) assume devices immersed in physical environments, significantly constrained in resources and expected to provide levels of security, privacy, reliability, performance and low power features. In recent years, numerous extra-functional aspects of electronic systems were brought to the front and imply verification of hardware design models in multidimensional space along with the functional concerns of the target system. However, different from the software domain such a holistic approach remains underdeveloped. The contributions of this paper are a taxonomy for multidimensional hardware verification aspects, a state-of-the-art survey of related research works and trends towards the multidimensional verification concept. The concept is motivated by an example for the functional and power verification dimensions.Comment: 2018 IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and International Symposium of System-on-Chip (SoC

    Understanding multidimensional verification: Where functional meets non-functional

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    Abstract Advancements in electronic systems' design have a notable impact on design verification technologies. The recent paradigms of Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) assume devices immersed in physical environments, significantly constrained in resources and expected to provide levels of security, privacy, reliability, performance and low-power features. In recent years, numerous extra-functional aspects of electronic systems were brought to the front and imply verification of hardware design models in multidimensional space along with the functional concerns of the target system. However, different from the software domain such a holistic approach remains underdeveloped. The contributions of this paper are a taxonomy for multidimensional hardware verification aspects, a state-of-the-art survey of related research works and trends enabling the multidimensional verification concept. Further, an initial approach to perform multidimensional verification based on machine learning techniques is evaluated. The importance and challenge of performing multidimensional verification is illustrated by an example case study
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