Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
VLSI and FPGA Design Techniques
Abstract
Artificial Intelligence (AI) is increasingly being applied to digital circuit design to assist with circuit analysis, optimization, and automation. This study examines the role of AI in digital circuit design, focusing on Graph Neural Networks (GNNs), Reinforcement Learning (RL), and Generative AI. These approaches can assist in analyzing circuit structures, exploring design alternatives, and improving factors such as power, performance, and area. The study also discusses applications of AI in circuit optimization, design space exploration, and automated circuit design. Despite its potential, AI-assisted circuit design faces challenges involving data availability, design verification, computational resources, and reliability. Overall, AI can serve as an assisting technology that complements conventional Electronic Design Automation (EDA) tools and supports the development of more efficient digital circuits.
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.
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Anh Nguyen-Duc, P. Abrahamsson· International Conference on...· 93 citations· ⚡9
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Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
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Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
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