SynQ: An Embedded DSL for Synchronous System Design with Quantitative Types

• URL: http://arxiv.org/abs/2505.02883v1
• Date: Mon, 05 May 2025 12:44:13 GMT
• Title: SynQ: An Embedded DSL for Synchronous System Design with Quantitative Types
• Authors: Rui Chen, Ingo Sander,
• Abstract summary: SynQ is an embedded domain specification language (EDSL) targeting the design of systems obeying the perfect synchrony hypothesis.<n>It enables a semantically coherent design process, including formal specification and verification, modelling, simulation and code generation.
• Score: 2.758944775979243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: System design automation aims to manage the design of embedded systems with ever-increasing complexity. To the success of system design automation, there is still a lack of systematic and formal design process because an entire design process, from a system's specification to its implementation, has to deal with inherent concerns about the systems' different aspects and, consequently, inherent semantic gaps. These gaps make it hard for a design process to be traceable or transparent. Particularly, guaranteeing the correctness of produced implementations becomes the main challenge for a system design process. SynQ (Synchronous system design with Quantitative types) is an embedded domain specification language (EDSL) targeting the design of systems obeying the perfect synchrony hypothesis. SynQ is based on a component-based design framework and, by design, facilitates semantic coherency by leveraging the quantitative type theory (QTT) and language embedding. SynQ enables a semantically coherent design process, including formal specification and verification, modelling, simulation and code generation. This paper presents SynQ and its underlying formalism and demonstrates its features and potential for semantically coherent system design through a case study.

Related papers

• Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation [72.44384066166147]
  Multi-agent systems (MAS) based on large language models (LLMs) have emerged as a powerful solution for dealing with complex problems across diverse domains.<n>Existing approaches are fundamentally constrained by their reliance on a template graph modification paradigm with a predefined set of agents and hard-coded interaction structures.<n>We propose ARG-Designer, a novel autoregressive model that operationalizes this paradigm by constructing the collaboration graph from scratch.
  arXiv  Detail & Related papers  (2025-07-24T09:17:41Z)
• Formation of requirements traceability in the process of information systems design [0.0]
  traceability of requirements in the information system design process is considered an essential property of the project.<n>One of the challenges of the traceability process, dubbed "The grand challenge of traceability" among traceability researchers, is its integration into the design process.<n>We propose the application of the Adaptive Clustering Method (ACM) of Information Systems developed by the author.
  arXiv  Detail & Related papers  (2025-05-07T08:13:59Z)
• Design Obligations for Software, with Examples from Data Abstraction and Adaptive Systems [1.2691047660244337]
  semantic expectations may apply not only at the code level, but also to more abstract system structures such as software architectures.<n>Unfortunately, these expectations are often implicit or documented only informally, so they are challenging to discover, let alone uphold.<n>I introduce the idea of 'design obligations', which are constraints on allowable designs within a given design paradigm that help to assure appropriate use of the paradigm.
  arXiv  Detail & Related papers  (2025-03-06T02:17:29Z)
• SYNTHIA: Novel Concept Design with Affordance Composition [114.19366716161655]
  We introduce SYNTHIA, a framework for generating novel, functionally coherent designs based on desired affordances.<n>We develop a curriculum learning scheme based on our ontology that contrast fine-tunes T2I models to progressively learn affordance composition.<n> Experimental results show that SYNTHIA outperforms state-of-the-art T2I models.
  arXiv  Detail & Related papers  (2025-02-25T02:54:11Z)
• A quantitative framework for evaluating architectural patterns in ML systems [49.1574468325115]
  This study proposes a framework for quantitative assessment of architectural patterns in ML systems.<n>We focus on scalability and performance metrics for cost-effective CPU-based inference.
  arXiv  Detail & Related papers  (2025-01-20T15:30:09Z)
• SynthAI: A Multi Agent Generative AI Framework for Automated Modular HLS Design Generation [0.0]
  We introduce SynthAI, a new method for the automated creation of High-Level Synthesis (HLS) designs. SynthAI integrates ReAct agents, Chain-of-Thought (CoT) prompting, web search technologies, and the Retrieval-Augmented Generation framework.
  arXiv  Detail & Related papers  (2024-05-25T05:45:55Z)
• Compositional Generative Inverse Design [69.22782875567547]
  Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem. We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples. In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
  arXiv  Detail & Related papers  (2024-01-24T01:33:39Z)
• Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
  This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE) The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
  arXiv  Detail & Related papers  (2022-11-29T17:28:31Z)
• Natural Language Processing for Systems Engineering: Automatic Generation of Systems Modelling Language Diagrams [0.10312968200748115]
  An approach is proposed to assist systems engineers in the automatic generation of systems diagrams from unstructured natural language text. The intention is to provide the users with a more standardised, comprehensive and automated starting point onto which subsequently refine and adapt the diagrams according to their needs.
  arXiv  Detail & Related papers  (2022-08-09T19:20:33Z)
• Graph Learning based Generative Design for Resilience of Interdependent Network Systems [3.6930948691311007]
  This study presents a generative design method that utilizes graph learning algorithms. The generator can intelligently mine good properties from existing systems and output new designs that meet predefined performance criteria. Case studies results based on power systems from the IEEE dataset have illustrated the applicability of the proposed method.
  arXiv  Detail & Related papers  (2022-07-03T01:35:08Z)
• Towards an Interface Description Template for AI-enabled Systems [77.34726150561087]
  Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components. There is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed. We present ongoing work on establishing an interface description template that captures the main information of an AI-enabled component.
  arXiv  Detail & Related papers  (2020-07-13T20:30:26Z)

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.