Zero-Shot RTL Code Generation with Attention Sink Augmented Large
Language Models
- URL: http://arxiv.org/abs/2401.08683v1
- Date: Fri, 12 Jan 2024 17:41:38 GMT
- Title: Zero-Shot RTL Code Generation with Attention Sink Augmented Large
Language Models
- Authors: Selim Sandal, Ismail Akturk
- Abstract summary: This paper discusses the possibility of exploiting large language models to streamline the code generation process in hardware design.
The ability to use large language models on RTL code generation not only expedites design cycles but also facilitates the exploration of design spaces.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The design and optimization of hardware have traditionally been
resource-intensive, demanding considerable expertise and dependence on
established design automation tools. This paper discusses the possibility of
exploiting large language models to streamline the code generation process in
hardware design. In contrast to earlier studies, this paper aims to use large
language models that accepts high-level design specifications through a single
prompt to generate corresponding Register-Transfer Level (RTL) code. The
ability to use large language models on RTL code generation not only expedites
design iteration cycles but also facilitates the exploration of design spaces
that have computational challenges for conventional techniques. Through our
evaluation, we demonstrate the shortcoming of existing attention mechanisms,
and present the abilities of language models to produce functional, optimized,
and industry-standard compliant RTL code when a novel attention mechanism is
used. These findings underscore the expanding role of large language models in
shaping the future landscape of architectural exploration and automation in
hardware design.
Related papers
- Natural language is not enough: Benchmarking multi-modal generative AI for Verilog generation [37.309663295844835]
We introduce an open-source benchmark for multi-modal generative models tailored for Verilog synthesis from visual-linguistic inputs.
We also introduce an open-source visual and natural language Verilog query language framework.
Our results demonstrate a significant improvement in the multi-modal generated Verilog compared to queries based solely on natural language.
arXiv Detail & Related papers (2024-07-11T13:10:09Z) - Natural Language to Verilog: Design of a Recurrent Spiking Neural Network using Large Language Models and ChatGPT [0.08388591755871733]
We employ OpenAI's ChatGPT4 and natural language prompts to synthesize a RTL Verilog module of a programmable recurrent spiking neural network.
The resultant design was validated in three case studies, the exclusive OR, the IRIS flower classification and the MNIST hand-written digit classification, achieving accuracies of up to 96.6%.
arXiv Detail & Related papers (2024-05-02T16:08:08Z) - LVLM-Interpret: An Interpretability Tool for Large Vision-Language Models [50.259006481656094]
We present a novel interactive application aimed towards understanding the internal mechanisms of large vision-language models.
Our interface is designed to enhance the interpretability of the image patches, which are instrumental in generating an answer.
We present a case study of how our application can aid in understanding failure mechanisms in a popular large multi-modal model: LLaVA.
arXiv Detail & Related papers (2024-04-03T23:57:34Z) - OMPGPT: A Generative Pre-trained Transformer Model for OpenMP [6.917568654215119]
OMPGPT is a novel domain-specific model meticulously designed to harness the inherent strengths of language models for OpenMP pragma generation.
We leverage prompt engineering techniques from the NLP domain to create Chain-of-OMP, an innovative strategy designed to enhance OMPGPT's effectiveness.
arXiv Detail & Related papers (2024-01-28T06:06:59Z) - LLM4EDA: Emerging Progress in Large Language Models for Electronic
Design Automation [74.7163199054881]
Large Language Models (LLMs) have demonstrated their capability in context understanding, logic reasoning and answer generation.
We present a systematic study on the application of LLMs in the EDA field.
We highlight the future research direction, focusing on applying LLMs in logic synthesis, physical design, multi-modal feature extraction and alignment of circuits.
arXiv Detail & Related papers (2023-12-28T15:09:14Z) - Formal Aspects of Language Modeling [74.16212987886013]
Large language models have become one of the most commonly deployed NLP inventions.
These notes are the accompaniment to the theoretical portion of the ETH Z"urich course on large language models.
arXiv Detail & Related papers (2023-11-07T20:21:42Z) - When Large Language Models Meet Personalization: Perspectives of
Challenges and Opportunities [60.5609416496429]
The capability of large language models has been dramatically improved.
Such a major leap-forward in general AI capacity will change the pattern of how personalization is conducted.
By leveraging large language models as general-purpose interface, personalization systems may compile user requests into plans.
arXiv Detail & Related papers (2023-07-31T02:48:56Z) - Opportunities for Large Language Models and Discourse in Engineering
Design [0.0]
We argue that discourse should be regarded as the core of engineering design processes, and therefore should be represented in a digital artifact.
We describe how simulations, experiments, topology optimizations, and other process steps can be integrated into a machine-actionable, discourse-centric design process.
arXiv Detail & Related papers (2023-06-15T14:46:44Z) - CodeTF: One-stop Transformer Library for State-of-the-art Code LLM [72.1638273937025]
We present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence.
Our library supports a collection of pretrained Code LLM models and popular code benchmarks.
We hope CodeTF is able to bridge the gap between machine learning/generative AI and software engineering.
arXiv Detail & Related papers (2023-05-31T05:24:48Z) - Language Models are General-Purpose Interfaces [109.45478241369655]
We propose to use language models as a general-purpose interface to various foundation models.
A collection of pretrained encoders perceive diverse modalities (such as vision, and language)
We propose a semi-causal language modeling objective to jointly pretrain the interface and the modular encoders.
arXiv Detail & Related papers (2022-06-13T17:34:22Z) - Deep Generative Models in Engineering Design: A Review [1.933681537640272]
We present a review and analysis of Deep Generative Learning models in engineering design.
Recent DGMs have shown promising results in design applications like structural optimization, materials design, and shape synthesis.
arXiv Detail & Related papers (2021-10-21T02:50:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.