Wolfgang Kunz

Deepak Narayan Gadde, Keerthan Kopparam Radhakrishna, Vaisakh Naduvodi Viswambharan et al.

Modern Integrated Circuits (ICs) are becoming increasingly complex, and so is their development process. Hardware design verification entails a methodical and disciplined approach to the planning, development, execution, and sign-off of functionally correct hardware designs. This tedious process requires significant effort and time to ensure a bug-free tape-out. The field of Natural Language Processing has undergone a significant transformation with the advent of Large Language Models (LLMs). These powerful models, often referred to as Generative AI (GenAI), have revolutionized how machines understand and generate human language, enabling unprecedented advancements in a wide array of applications, including hardware design verification. This paper presents an agentic AI-based approach to hardware design verification, which empowers AI agents, in collaboration with Humain-in-the-Loop (HITL) intervention, to engage in a more dynamic, iterative, and self-reflective process, ultimately performing end-to-end hardware design and verification. This methodology is evaluated on five open-source designs, achieving over 95% coverage with reduced verification time while demonstrating superior performance, adaptability, and configurability.

Mohammad Rahmani Fadiheh, Alex Wezel, Johannes Mueller et al.

Hardware (HW) security issues have been emerging at an alarming rate in recent years. Transient execution attacks, in particular, pose a genuine threat to the security of modern computing systems. Despite recent advances, understanding the intricate implications of microarchitectural design decisions on processor security remains a great challenge and has caused a number of update cycles in the past. number of update cycles in the past. This papers addresses the need for a new approach to HW sign-off verification which guarantees the security of processors at the Register Transfer Level (RTL). To this end, we introduce a formal definition of security with respect to transient execution attacks, formulated as a HW property. We present a formal proof methodology based on Unique Program Execution Checking (UPEC) which can be used to systematically detect all vulnerabilities to transient execution attacks in RTL designs. UPEC does not exploit any a priori knowledge on known attacks and can therefore detect also vulnerabilities based on new, so far unknown, types of channels. This is demonstrated by two new attack scenarios discovered in our experiments with UPEC. UPEC scales to a wide range of HW designs, including in-order processors (RocketChip), pipelines with out-of-order writeback (Ariane), and processors with deep out-of-order speculative execution (BOOM). To the best of our knowledge, UPEC is the first RTL verification technique that exhaustively covers transient execution side channels in processors of realistic complexity.

M. Ammar Ben Khadra, Dominik Stoffel, Wolfgang Kunz

Code coverage analysis plays an important role in the software testing process. More recently, the remarkable effectiveness of coverage feedback has triggered a broad interest in feedback-guided fuzzing. In this work, we introduce bcov, a tool for binary-level coverage analysis. Our tool statically instruments x86-64 binaries in the ELF format without compiler support. We implement several techniques to improve efficiency and scale to large real-world software. First, we bring Agrawal's probe pruning technique to binary-level instrumentation and effectively leverage its superblocks to reduce overhead. Second, we introduce sliced microexecution, a robust technique for jump table analysis which improves CFG precision and enables us to instrument jump table entries. Additionally, smaller instructions in x86-64 pose a challenge for inserting detours. To address this challenge, we aggressively exploit padding bytes and systematically host detours in neighboring basic blocks. We evaluate bcov on a corpus of 95 binaries compiled from eight popular and well-tested packages like FFmpeg and LLVM. Two instrumentation policies, with different edge-level precision, are used to patch all functions in this corpus - over 1.6 million functions. Our precise policy has average performance and memory overheads of 14% and 22% respectively. Instrumented binaries do not introduce any test regressions. The reported coverage is highly accurate with an average F-score of 99.86%. Finally, our jump table analysis is comparable to that of IDA Pro on gcc binaries and outperforms it on clang binaries.