2/18/2026 | USA | technology | ✓ Verified - arxiv.org

GenAI for Systems: Recurring Challenges and Design Principles from Software to Silicon

#Generative AI #System design #Software stack #Hardware architecture #Chip design #Code generation #Distributed runtimes #Design space exploration #RTL synthesis #Physical layout #Verification #Cross‑stack perspective

📌 Key Takeaways

Generative AI is changing how computing systems are designed, optimised, and built.
Research in this area is fragmented across software, architecture, and chip‑design communities.
The paper offers a cross‑stack perspective that covers code generation, distributed runtimes, hardware design space exploration, RTL synthesis, physical layout and verification.
It identifies recurring challenges and proposes design principles that apply across all these layers.
The study aims to bridge the gaps between software and silicon design practices.
The research was submitted to arXiv in February 2026, making it a recent contribution to the field.

📖 Full Retelling

The authors present a cross‑stack perspective on how generative AI is reshaping the design, optimization and fabrication of computing systems. Published on arXiv in February 2026, the paper identifies the fragmented state of research across software, architecture and chip‑design communities and offers a unified view of recurring challenges and design principles that span from code generation and distributed runtimes to hardware design space exploration, RTL synthesis, physical layout and verification. The work underscores the need for a holistic approach that bridges these layers, arguing that generative models can accelerate progress at every stage of the system development lifecycle while also revealing common obstacles that transcend traditional disciplinary boundaries.

🏷️ Themes

Generative AI in Systems Design, Cross‑Stack Integration, Research Fragmentation, Design Principles, From Code to Silicon

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Deep Analysis

Why It Matters

Generative AI is transforming system design from software to silicon, enabling faster prototyping and optimization across the stack. This integration can reduce development time and improve performance, but also introduces new challenges in consistency and reliability.

Context & Background

Fragmentation between software, architecture, and chip design communities
Current research focuses on isolated layers such as code generation or RTL synthesis
Need for a unified cross‑stack approach to leverage AI throughout system development

What Happens Next

Future work will focus on integrating AI models into end‑to‑end design pipelines, establishing best practices for verification, and creating shared benchmarks. Industry adoption will likely accelerate as tools mature and demonstrate tangible gains.

Frequently Asked Questions

What is the main benefit of applying generative AI to hardware design?

It can automate exploration of design space, generate RTL code, and accelerate synthesis, leading to faster time‑to‑market.

What challenges remain in adopting AI across the stack?

Ensuring model reliability, managing data privacy, and aligning AI outputs with rigorous verification standards.

Original Source

              arXiv:2602.15241v1 Announce Type: cross 
Abstract: Generative AI is reshaping how computing systems are designed, optimized, and built, yet research remains fragmented across software, architecture, and chip design communities. This paper takes a cross-stack perspective, examining how generative models are being applied from code generation and distributed runtimes through hardware design space exploration to RTL synthesis, physical layout, and verification. Rather than reviewing each layer in i
            

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Source

arxiv.org