Related papers: RV32I in ACL2
The development of safety applications for Connected Automated Vehicles requires testing in many different scenarios. However, the recreation of test scenarios for evaluating safety applications is a very challenging task. This is mainly…
There has been great progress recently in formally specifying the memory model of microprocessors like ARM and POWER. These specifications are, however, too complicated for reasoning about program behaviors, verifying compilers etc.,…
With the recent success of embeddings in natural language processing, research has been conducted into applying similar methods to code analysis. Most works attempt to process the code directly or use a syntactic tree representation,…
We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data. We induce a pseudo language as a compact discrete representation, and formulate a self-supervised pseudo speech…
While functional RISC-V implementations are readily available in academia, controlled empirical studies that extend a single baseline architecture along multiple design axes and quantify the resulting trade-offs at each step remain scarce.…
Reliability has been a major concern in embedded systems. Higher transistor density and lower voltage supply increase the vulnerability of embedded systems to soft errors. A Single Event Upset (SEU), which is also called a soft error, can…
Visual Speech Recognition (VSR) differs from the common perception tasks as it requires deeper reasoning over the video sequence, even by human experts. Despite the recent advances in VSR, current approaches rely on labeled data to fully…
We introduce the Block-Recurrent Transformer, which applies a transformer layer in a recurrent fashion along a sequence, and has linear complexity with respect to sequence length. Our recurrent cell operates on blocks of tokens rather than…
Recently DeepSeek R1 demonstrated how reinforcement learning with simple rule-based incentives can enable autonomous development of complex reasoning in large language models, characterized by the "aha moment", in which the model manifest…
By exploiting the modular RISC-V ISA this paper presents the customization of instruction set with posit\textsuperscript{\texttrademark} arithmetic instructions to provide improved numerical accuracy, well-defined behavior and increased…
When ACL2 is used to model the operational semantics of computing machines, machine states are typically represented by terms recording the contents of the state components. When models are realistic and are stepped through thousands of…
We present the first framework for efficient application of stateless model checking (SMC) to programs running under the relaxed memory model of POWER. The framework combines several contributions. The first contribution is that we develop…
Almost all Computer Science programs require students to take a course on the Theory of Computation (ToC) which covers various models of computation such as finite automata, push-down automata and Turing machines. ToC courses tend to give…
Large language models (LLMs) excel at reasoning, yet post-training remains critical for aligning their behavior with task goals. Existing reinforcement learning (RL) methods often depend on costly human annotations or external reward…
Education in the practical applications of logic and proving such as the formal specification and verification of computer programs is substantially hampered by the fact that most time and effort that is invested in proving is actually…
Vision-language models (VLMs) are widely assumed to exhibit in-context learning (ICL), a property similar to that of their language-only counterparts. While recent work suggests VLMs can perform multimodal ICL (MM-ICL), studies show they…
Growing concerns regarding the operational usage of AI models in the real-world has caused a surge of interest in explaining AI models' decisions to humans. Reinforcement Learning is not an exception in this regard. In this work, we propose…
Vision-Language-Action models (VLAs) mark a major shift in robot learning. They replace specialized architectures and task-tailored components of expert policies with large-scale data collection and setup-specific fine-tuning. In this…
In the last decade, we have witnessed exponential growth in the complexity of control systems for safety-critical applications (automotive, robots, industrial automation) and their transition to heterogeneous mixed-criticality systems…
This paper presents the verification of control systems implemented in Simulink. The goal is to ensure that high-level requirements on control performance, like stability, are satisfied by the Simulink diagram. A two stage process is…