Chip-Chat: Challenges and Opportunities in Conversational Hardware Design

Modern hardware design starts with specifications provided in natural language. These are then translated by hardware engineers into appropriate Hardware Description Languages (HDLs) such as Verilog before synthesizing circuit elements. Automating this translation could reduce sources of human error from the engineering process. But, it is only recently that artificial intelligence (AI) has demonstrated capabilities for machine-based end-to-end design translations. Commercially-available instruction-tuned Large Language Models (LLMs) such as OpenAI's ChatGPT and Google's Bard claim to be able to produce code in a variety of programming languages; but studies examining them for hardware are still lacking. In this work, we thus explore the challenges faced and opportunities presented when leveraging these recent advances in LLMs for hardware design. Given that these `conversational' LLMs perform best when used interactively, we perform a case study where a hardware engineer co-architects a novel 8-bit accumulator-based microprocessor architecture with the LLM according to real-world hardware constraints. We then sent the processor to tapeout in a Skywater 130nm shuttle, meaning that this `Chip-Chat' resulted in what we believe to be the world's first wholly-AI-written HDL for tapeout.Comment: 6 pages, 8 figures. Accepted in 2023 ACM/IEEE 5th Workshop on Machine Learning for CAD (MLCAD

P15. Employing students' multilingualism and language diversity in teaching and learning

Before our innovative clinical skills session ‘Interpreting in Consultations’, we conducted an annual survey of languages spoken by students on admission, in 2006, 2007 and 2008. Froma response rate of 94% we noted that 28% of students are advanced/fluent speakers of language(s) other than English and a total of 48 languages are spoken.The session, ‘Interpreting in Consultations’, involves first and second year students who speak the same language other than English, role-playing an ‘interpreted’ consultation.Feedback from tutors and students following the session shows that using different languages serves multiple, valuable purposes, highlighting:• issues encountered with interpreters• challenges of ‘medical’ language• difficulties in transmitting a patient centred approach• how linguistic and cultural sensitivities are lost in translation.Student linguistic diversity is considerable and not used to its full potential: the single clinical skills session we report suggests there is much more to be gained. The education we design and delivermay fail to recognise what patient-centred-ness means in different languages and cultures.Future research should: consider how to make best use of multiculturalism and linguistic diversity; explore how students’ awareness of, and competence in, different languages and culturescan be developed and maintained

Dcc --help: Generating Context-Aware Compiler Error Explanations with Large Language Models

In the challenging field of introductory programming, high enrollments and failure rates drive us to explore tools and systems to enhance student outcomes, especially automated tools that scale to large cohorts. This paper presents and evaluates the dcc --help tool, an integration of a Large Language Model (LLM) into the Debugging C Compiler (DCC) to generate unique, novice-focused explanations tailored to each error. dcc --help prompts an LLM with contextual information of compile- and run-time error occurrences, including the source code, error location and standard compiler error message. The LLM is instructed to generate novice-focused, actionable error explanations and guidance, designed to help students understand and resolve problems without providing solutions. dcc --help was deployed to our CS1 and CS2 courses, with 2,565 students using the tool over 64,000 times in ten weeks. We analysed a subset of these error/explanation pairs to evaluate their properties, including conceptual correctness, relevancy, and overall quality. We found that the LLM-generated explanations were conceptually accurate in 90% of compile-time and 75% of run-time cases, but often disregarded the instruction not to provide solutions in code. Our findings, observations and reflections following deployment indicate that dcc-help provides novel opportunities for scaffolding students' introduction to programming.Comment: 7 pages, 2 figures. Accepted in SIGCSE'2

FLAG: Finding Line Anomalies (in code) with Generative AI

Code contains security and functional bugs. The process of identifying and localizing them is difficult and relies on human labor. In this work, we present a novel approach (FLAG) to assist human debuggers. FLAG is based on the lexical capabilities of generative AI, specifically, Large Language Models (LLMs). Here, we input a code file then extract and regenerate each line within that file for self-comparison. By comparing the original code with an LLM-generated alternative, we can flag notable differences as anomalies for further inspection, with features such as distance from comments and LLM confidence also aiding this classification. This reduces the inspection search space for the designer. Unlike other automated approaches in this area, FLAG is language-agnostic, can work on incomplete (and even non-compiling) code and requires no creation of security properties, functional tests or definition of rules. In this work, we explore the features that help LLMs in this classification and evaluate the performance of FLAG on known bugs. We use 121 benchmarks across C, Python and Verilog; with each benchmark containing a known security or functional weakness. We conduct the experiments using two state of the art LLMs in OpenAI's code-davinci-002 and gpt-3.5-turbo, but our approach may be used by other models. FLAG can identify 101 of the defects and helps reduce the search space to 12-17% of source code

Designing Neural Networks for Real-Time Systems

Artificial Neural Networks (ANNs) are increasingly being used within safety-critical Cyber-Physical Systems (CPSs). They are often co-located with traditional embedded software, and may perform advisory or control-based roles. It is important to validate both the timing and functional correctness of these systems. However, most approaches in the literature consider guaranteeing only the functionality of ANN based controllers. This issue stems largely from the implementation strategies used within common neural network frameworks -- their underlying source code is often simply unsuitable for formal techniques such as static timing analysis. As a result, developers of safety-critical CPS must rely on informal techniques such as measurement based approaches to prove correctness, techniques that provide weak guarantees at best. In this work we address this challenge. We propose a design pipeline whereby neural networks trained using the popular deep learning framework Keras are compiled to functionally equivalent C code. This C code is restricted to simple constructs that may be analysed by existing static timing analysis tools. As a result, if compiled to a suitable time-predictable platform all execution bounds may be statically derived. To demonstrate the benefits of our approach we execute an ANN trained to drive an autonomous vehicle around a race track. We compile the ANN to the Patmos time-predictable controller, and show that we can derive worst case execution timings.Comment: 4 pages, 2 figures. IEEE Embedded Systems Letters, 202

VeriGen: A Large Language Model for Verilog Code Generation

In this study, we explore the capability of Large Language Models (LLMs) to automate hardware design by generating high-quality Verilog code, a common language for designing and modeling digital systems. We fine-tune pre-existing LLMs on Verilog datasets compiled from GitHub and Verilog textbooks. We evaluate the functional correctness of the generated Verilog code using a specially designed test suite, featuring a custom problem set and testing benches. Here, our fine-tuned open-source CodeGen-16B model outperforms the commercial state-of-the-art GPT-3.5-turbo model with a 1.1% overall increase. Upon testing with a more diverse and complex problem set, we find that the fine-tuned model shows competitive performance against state-of-the-art gpt-3.5-turbo, excelling in certain scenarios. Notably, it demonstrates a 41% improvement in generating syntactically correct Verilog code across various problem categories compared to its pre-trained counterpart, highlighting the potential of smaller, in-house LLMs in hardware design automation.Comment: arXiv admin note: text overlap with arXiv:2212.1114

Reactivation of Fault Systems by Compartmentalized Hydrothermal Fluids in the Southern Andes Revealed by Magnetotelluric and Seismic Data

In active volcanic arcs such as the Andean volcanic mountain belt, magmatically‐sourced fluids are channelled through the brittle crust by faults and fracture networks. In the Andes, volcanoes, geothermal springs and major mineral deposits have a spatial and genetic relationship with NNE‐trending, margin‐parallel faults and margin‐oblique, NW‐trending Andean Transverse Faults (ATF). The Tinguiririca and Planchón‐Peteroa volcanoes in the Andean Southern Volcanic Zone (SVZ) demonstrate this relationship, as their spatially associated thermal springs show strike alignment to the NNE‐oriented El Fierro Thrust Fault System. We constrain the fault system architecture and its interaction with volcanically sourced hydrothermal fluids using a combined magnetotelluric (MT) and seismic survey that was deployed for 20 months. High conductivity zones are located along the axis of the active volcanic chain, delineating fluids and/or melt. A distinct WNW‐trending cluster of seismicity correlates with resistivity contrasts, considered to be a reactivated ATF. Seismicity occurs below 4 km, suggesting activity is limited to basement rocks, and the cessation of seismicity at 9 km delineates the local brittle‐ductile transition. As seismicity is not seen west of the El Fierro fault, we hypothesize that this structure plays a key role in compartmentalizing magmatically‐derived hydrothermal fluids to the east, where the fault zone acts as a barrier to cross‐fault fluid migration and channels fault‐parallel fluid flow to the surface from depth. Increases in fluid pressure above hydrostatic may facilitate reactivation. This site‐specific case study provides the first three‐dimensional seismic and magnetotelluric observations of the mechanics behind the reactivation of an ATF

Needle in a Haystack: Detecting Subtle Malicious Edits to Additive Manufacturing G-code Files

Increasing usage of Digital Manufacturing (DM) in safety-critical domains is increasing attention on the cybersecurity of the manufacturing process, as malicious third parties might aim to introduce defects in digital designs. In general, the DM process involves creating a digital object (as CAD files) before using a slicer program to convert the models into printing instructions (e.g. g-code) suitable for the target printer. As the g-code is an intermediate machine format, malicious edits may be difficult to detect, especially when the golden (original) models are not available to the manufacturer. In this work we aim to quantify this hypothesis through a red-team/blue-team case study, whereby the red-team aims to introduce subtle defects that would impact the properties (strengths) of the 3D printed parts, and the blue-team aims to detect these modifications in the absence of the golden models. The case study had two sets of models, the first with 180 designs (with 2 compromised using 2 methods) and the second with 4320 designs (with 60 compromised using 6 methods). Using statistical modelling and machine learning (ML), the blue-team was able to detect all the compromises in the first set of data, and 50 of the compromises in the second

Potential mainland Chinese cruise travelers’ expectations, motivations, and intentions

The global cruise industry is the fastest growing sector in the entire leisure market. Due to the limited development of the Chinese cruise sector and government controls on outbound travel, the cruise, especially the outbound cruise, is a new concept in China. Few studies have addressed Chinese consumers’ perceptions of cruises. This study aimed to explore the preferences of potential Chinese cruisers and their expectations, motivations, and intentions in relation to taking an outbound cruise. This study also proposed and tested a conceptual framework: the Expectation, Motivation, and Intention (EMI) Model. Data were collected in Beijing and Shanghai; 242 valid responses were received. The results partially supported the proposed model. The theoretical and practical contributions of the study are discussed