SHIA: A Direct SysML-Hardware Interface Architecture for Model-Centric Verification
- URL: http://arxiv.org/abs/2605.11248v1
- Date: Mon, 11 May 2026 21:16:27 GMT
- Title: SHIA: A Direct SysML-Hardware Interface Architecture for Model-Centric Verification
- Abstract summary: This paper introduces the SysML Hardware Interface Architecture (SHIA), which keeps an executable SysML model directly inside the verification loop.<n>SHIA is realised through a SysML side server, written in embedded C++ within IBM Rhapsody, and a hardware side server running on a Raspberry Pi.<n>A logic gate case study demonstrates the approach end-to-end, from hardware model construction and prototype assembly to test harness design, behavioural statechart control, and staged verification of each component before integration.
- Score: 0.5604402179938209
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Model-Based Systems Engineering (MBSE) is widely treated as the backbone of digital engineering, with languages such as the Systems Modeling Language (SysML) providing the means to capture system structure, behaviour, and verification intent. Yet once verification moves to hardware, the system model is routinely left behind. Domain-specific simulation environments, model transformations, and bespoke tool integrations take over, and the model that began as the authoritative reference drifts out of sync with the implementation it was meant to govern. This paper introduces the SysML Hardware Interface Architecture (SHIA), which keeps an executable SysML model directly inside the verification loop, exchanging messages with physical hardware without intermediate transformation chains, co-simulation platforms, or broker-mediated plugins. SHIA is realised through a SysML side server, written in embedded C++ within IBM Rhapsody, and a hardware side server running on a Raspberry Pi, together establishing a bidirectional link between the digital model and the physical system. A logic gate case study demonstrates the approach end-to-end, from hardware model construction and prototype assembly to test harness design, behavioural statechart control, and staged verification of each component before integration. The integrated system exchanged messages correctly in both directions, and Karnaugh map comparison between the SysML-generated and hardware-generated outputs showed zero discrepancy. The result shows that, when paired with a suitable interface, SysML need not remain a static description that informs downstream tools; it can serve as the executable layer through which hardware behaviour is stimulated, observed, and verified. The work demonstrates a route to model-governed verification and a shorter digital thread between system architecture and the hardware that realises it.
Related papers
- LLM-based Hardware Development with Hierarchical IRs and End-to-End Multi-Agent Workflow [3.3126072964592836]
Large language models (LLMs) are increasingly used in software development, but their use in complex hardware design remains limited.<n>We present an LLM-based hardware development framework with hierarchical intermediate representations (IRs) and an end-to-end multi-agent workflow.<n>We evaluate our framework on Verilog-Eval benchmark, achieving a pass@5 rate of 95.5%, which surpasses current state-of-the-art LLM generation frameworks.
arXiv Detail & Related papers (2026-08-31T11:57:37Z) - SAMpLE: A SystemC-AMS Machine LEarning-based Framework for Virtual Prototyping [1.4932318540666545]
This paper presents textbftextitSAMpLE, an open-source framework that integrates Machine Learning models as first-class Timed Dataflow components.<n>The framework uses ONNX as a standard model exchange format to enable integration of externally trained ML models into SystemC-AMS simulations.
arXiv Detail & Related papers (2026-08-26T15:26:21Z) - LLM-Driven, Datasheet-Aware Automated Hardware Compatibility Verification for Early-Stage, Pre-Schematic Embedded System Design [8.89632807938116]
We present an LLM-driven, computation-aware framework for early-stage hardware compatibility verification.<n>It does not require, and can therefore be used, before detailed schematic simulation and implementation.
arXiv Detail & Related papers (2026-08-25T23:12:48Z) - EquivFusion: Unifying Hardware Equivalence Checking from Algorithms to Netlists via MLIR [14.681439854488765]
EquivFusion is an end-to-end equivalence checking tool tailored for multi-modal circuit designs.<n>It unifies diverse entry points, including PyTorch, C/C++, Chisel, Verilog, and gate-level netlists, into a common intermediate representation.
arXiv Detail & Related papers (2026-04-17T12:09:54Z) - AutoFlows++: Hierarchical Message Flow Mining for System on Chip Designs [6.0174402828965725]
AutoFlows++ is a design-architecture-guided hierarchical framework for mining message flows from communication traces of complex designs.<n>Results show that AutoFlows++ significantly improves flow extraction accuracy compared with prior approaches.
arXiv Detail & Related papers (2026-04-12T15:44:43Z) - Sketch2Simulation: Automating Flowsheet Generation via Multi Agent Large Language Models [0.0]
Converting process sketches into executable simulation models remains a major bottleneck in process systems engineering.<n>Recent advances in generative AI have improved both engineering-diagram interpretation and flowsheet generation, but these remain largely disconnected.<n>We present an end-to-end multi-agent large language model system that converts process diagrams directly into executable Aspen HYSYS flowsheets.
arXiv Detail & Related papers (2026-03-25T03:36:46Z) - Hardware Co-Design Scaling Laws via Roofline Modelling for On-Device LLMs [49.99513618431772]
We propose a hardware co-design law that captures model accuracy and inference performance.<n>We empirically evaluate 1,942 candidate architectures on NVIDIA Jetson Orin.<n>Our architecture achieves 19.42% lower perplexity on WikiText-2.
arXiv Detail & Related papers (2026-02-10T23:51:00Z) - An Architecture-Led Hybrid Report on Body Language Detection Project [0.0]
This report provides an architecture-led analysis of two modern vision-language models (VLMs)<n>It explains how their architectural properties map to a practical video-to-artifact pipeline implemented in the BodyLanguageDetection.
arXiv Detail & Related papers (2025-12-28T18:03:00Z) - Executable Ontologies: Synthesizing Event Semantics with Dataflow Architecture [51.56484100374058]
We demonstrate that integrating semantic event semantics with a dataflow architecture addresses the limitations of traditional Business Process Management systems.<n>The boldsea-engine's architecture interprets semantic models as executable algorithms without compilation.<n>It enables the modification of event models at runtime ensures transparency, and seamlessly merges data and business logic within a unified semantic framework.
arXiv Detail & Related papers (2025-09-11T18:12:46Z) - The Scalability of Simplicity: Empirical Analysis of Vision-Language Learning with a Single Transformer [68.71557348281007]
This paper introduces SAIL, a single transformer unified multimodal large language model (MLLM)<n>Unlike existing modular MLLMs, which rely on a pre-trained vision transformer (ViT), SAIL eliminates the need for a separate vision encoder.<n>We systematically compare SAIL's properties-including scalability, cross-modal information flow patterns, and visual representation capabilities-with those of modular MLLMs.
arXiv Detail & Related papers (2025-04-14T17:50:20Z) - Designing and Implementing a Generator Framework for a SIMD Abstraction Library [53.84310825081338]
We present TSLGen, a novel end-to-end framework for generating an SIMD abstraction library.
We show that our framework is comparable to existing libraries, and we achieve the same performance results.
arXiv Detail & Related papers (2024-07-26T13:25:38Z) - Object Files and Schemata: Factorizing Declarative and Procedural
Knowledge in Dynamical Systems [135.10772866688404]
Black-box models with a monolithic hidden state often fail to apply procedural knowledge consistently and uniformly.
We address this issue via an architecture that factorizes declarative and procedural knowledge.
arXiv Detail & Related papers (2020-06-29T17:45:03Z) - Taurus: A Data Plane Architecture for Per-Packet ML [59.1343317736213]
We present the design and implementation of Taurus, a data plane for line-rate inference.
Our evaluation of a Taurus switch ASIC shows that Taurus operates orders of magnitude faster than a server-based control plane.
arXiv Detail & Related papers (2020-02-12T09:18:36Z)
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.