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Hardware security verification in hardware design has been identified as a significant bottleneck due to complexity and time-to-market constraints. Assertion-Based Verification is a recognized solution to this challenge; however, assertion generation relies on expertise and labor. While LLMs show promise as automated tools, existing approaches often rely on complex prompt engineering, requiring expert validation. The challenge lies in identifying effective methods for constructing training datasets that enhance LLMs' hardware performance. We introduce HADA (Hardware Assertion through Data Augmentation), a novel framework to train hardware debug specific expert LLM by leveraging its ability to integrate knowledge from formal verification tools, hardware security knowledge database, and version control system. Our results demonstrate that integrating multi-source data significantly enhances the effectiveness of hardware security verification, with each addressing the limitations of the others. 

This paper presents LLM4SecHW, a novel framework for hardware debugging that leverages domain-specific Large Language Model (LLM). Despite the success of LLMs in automating various software development tasks, their application in the hardware security domain has been limited due to the constraints of commercial LLMs and the scarcity of domain-specific data. To address these challenges, we propose a unique approach to compile a dataset of open-source hardware design defects and their remediation steps, utilizing version control data. This dataset provides a substantial foundation for training machine learning models for hardware. LLM4SecHW employs fine-tuning of medium-sized LLMs based on this dataset, enabling the identification and rectification of bugs in hardware designs. This pioneering approach offers a reference workflow for the application of fine-tuning domain-specific LLMs in other research areas. We evaluate the performance of our proposed system on various open-source hardware designs, demonstrating its efficacy in accurately identifying and correcting defects. Our work brings a new perspective on automating the quality control process in hardware design. 

The Polymorphic Circuit Interoperability Framework is presented in this paper. This framework separates the polymorphic component (called the polymorphic element) from the functional gates (called the switchable gate). The requirement of the framework is that the polymorphic element outputs a non-empty set of signals that change based on the polymorphic effect desired. In this paper, single output polymorphic elements based on voltage and clock speed are shown, and a polymorphic element based on temperature is theoretically adapted from existing literature [5]. A switchable gate that implements NAND/NOR functionality is shown and used with these polymorphic elements to test the framework for polymorphic functionality. The results are presented and polymorphic functionality is successfully demonstrated. 

Polymorphic gates are reconfigurable devices whose functionality may vary in response to the change of execution environment such as temperature, supply voltage or external control signals. This feature makes them a perfect candidate for circuit watermarking. However, polymorphic gates are hard to find because they do not exhibit the traditional structure. In this paper, we report four dual-function polymorphic gates that we have discovered using an evolutionary approach. With these gates, we propose a circuit watermarking scheme that selectively replace certain regular logic gates by the polymorphic gates. Experimental results on ISCAS and MCNC benchmark circuits demonstrate that this scheme introduce low overhead. More specifically, the average overhead in area, speed and power are 4.10%, 2.08% and 1.17% respectively when we embed 30-bit watermark sequences. These overhead increase to 6.36%, 4.75% and 2.08% respectively when 10% of the gates in the original circuits are replaced to embed watermark up to more than 300 bits.