Imec’s Patrick Vandenameele: Full-stack Innovation Is the Name of the Game

2026-05-19 · Source: Big Data & AI News - EE Times · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, medium

Summary

Imec CEO Patrick Vandenameele, ahead of ITF World 2026, detailed five technology shifts for the coming decade: system-wide co-optimization, silicon photonics, memory's strategic importance, chiplets in edge computing, and quantum computing industrialization. He stressed that future semiconductor progress, especially for advanced AI, requires "full-stack innovation" and deep cross-technology co-optimization (XTCO) across the entire computing stack, moving beyond traditional transistor shrinks. This "CMOS 2.0" approach demands tighter integration of compute, memory, packaging, and photonics, plus increased collaboration among foundries, hyperscalers, and AI architects. Imec is expanding its role, launching imec.AI-labs in Paris to benchmark hardware for AI algorithms. Vandenameele highlighted silicon photonics nearing a commercial tipping point by 2030, transitioning to in-interposer photonics for "scale-in" architectures. Memory and interconnects are critical bottlenecks, requiring breakthroughs like 3D integration and embedded DRAM. Imec will also unveil the first qubit fabricated using ASML's High-NA EUV lithography.

Key takeaway

For AI Architects and Hardware Engineers designing next-generation AI systems, recognize that traditional scaling is insufficient. Your focus must shift to deep, full-stack co-optimization (XTCO) across compute, memory, and photonics to meet AI's extreme energy and bandwidth demands. Prioritize integrating silicon photonics closer to compute and exploring advanced memory solutions like embedded DRAM. Engage in broader ecosystem collaborations to accelerate breakthroughs, as isolated efforts will increasingly bottleneck performance and efficiency.

Key insights

Future AI progress depends on deep, full-stack co-optimization across compute, memory, photonics, and packaging.

Principles

• Significant gains come from system-wide co-optimization.
• AI scaling demands major hardware breakthroughs.
• Deep ecosystem collaboration is crucial for complex systems.

Method

The XTCO framework integrates compute, memory, packaging, photonics, and AI model architectures for cross-technology co-optimization.

In practice

• Optimize technologies specifically for high TOPS AI workloads.
• Transition to in-interposer photonics for "scale-in" architectures.
• Customize DRAM and explore embedded DRAM for memory bottlenecks.

Topics

• Full-stack Co-optimization
• AI Hardware
• Silicon Photonics
• Quantum Computing
• Memory Technologies
• High-NA EUV

Best for: Research Scientist, AI Hardware Engineer, AI Architect, AI Scientist

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