Imec Says AI Scaling Needs More Orchestration Across Research, Design, Manufacturing

2026-05-22 · Source: Big Data & AI News - EE Times · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Robotics & Autonomous Systems · Depth: Intermediate, long

Summary

At ITF World 2026, Imec CEO Patrick Vandenameele asserted that scaling AI necessitates comprehensive "orchestration" across research, design, engineering, and manufacturing, likening it to an orchestra. Microsoft's Rani Borkar echoed this, stressing the need for co-development and co-optimization across every layer of the stack, advocating for greater standardization in the current "wild west" AI data center ecosystem. The conference also highlighted Imec.ventures' efforts to build a global network for commercializing research and supporting startups, encouraging a global perspective beyond national flags. Additionally, Neuropixels 3.0, a project integrating neuroscience and semiconductors with MEMS sensors and CMOS technology for brain activity studies, was demonstrated. Other key areas included chiplets for autonomous edge, CMOS 2.0 for ultra-dense vertical interconnects, and optical interconnects for faster data exchange.

Key takeaway

For AI Architects and Semiconductor Executives focused on scaling AI infrastructure, recognize that isolated development is unsustainable. You must prioritize deep co-design and co-optimization across hardware, software, and models, engaging with partners from research to manufacturing. Advocate for greater standardization in the AI data center ecosystem to reduce complexity and accelerate progress. Embrace global collaboration for technology development and commercialization, moving beyond nationalistic approaches to foster innovation.

Key insights

Scaling AI demands deep, multi-stakeholder collaboration and standardization across the entire technology stack, from research to manufacturing.

Principles

• Scaling AI requires technology innovation, system design, engineering, and manufacturing to work in concert.
• Co-design and co-optimization across hardware, software, and models are essential for advanced systems.
• Standardization is crucial in the AI data center ecosystem to move beyond a "wild west" environment.

In practice

• Co-develop and co-optimize at every layer of the stack for multi-modal, multi-model AI.
• Seek greater standardization in AI data center infrastructure where it is currently lacking.
• Foster global partnerships for technology commercialization and startup scaling.

Topics

• AI Scaling
• Semiconductor Industry
• Hardware-Software Co-optimization
• Data Center Standardization
• Neuropixels
• Chiplets
• Global Technology Collaboration

Best for: AI Architect, AI Hardware Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Big Data & AI News - EE Times.