The Changing ASICs Landscape: the Shift Toward Chip Disaggregation

2026-04-21 · Source: Big Data & AI News - EE Times · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, medium

Summary

The rise of AI is driving a significant shift in ASIC design, moving from monolithic, standard-driven chips to highly system-specific, disaggregated architectures. This change is necessitated by the diverse, compute-intensive nature of AI workloads, which demand silicon tailored to specific algorithms, data flows, and deployment environments. Disaggregation breaks down complex ASICs into multiple smaller, optimized dies, enabling hyper-specialization and addressing challenges like long redesign cycles and high costs associated with monolithic designs. Advanced packaging technologies, such as wafer-to-wafer bonding and 2.5D integration, are crucial enablers for integrating these specialized dies, improving power, performance, area (PPA), and time to market. This evolution requires extensive cross-domain collaboration spanning architecture, packaging, and manufacturing, moving beyond traditional chip-centric design flows to holistic co-design approaches.

Key takeaway

For CTOs and VPs of Engineering navigating AI hardware strategy, recognize that system-specific, disaggregated ASICs are becoming essential for optimizing performance, power, and cost. Your teams should prioritize deep cross-domain collaboration between architecture, packaging, and manufacturing, and invest in reusable IP and reference designs to manage the complexity and accelerate time to market for next-generation AI systems.

Key insights

AI workloads drive ASICs towards system-specific, disaggregated architectures, demanding cross-domain collaboration and advanced packaging.

Principles

• Performance is tied to silicon's fit with algorithms and data flows.
• Disaggregation enables hyper-specialization and efficiency.
• System-level design is critical for multi-die ASICs.

Method

Disaggregate ASICs into smaller, function-optimized dies; integrate using advanced packaging (e.g., 2.5D); validate with reference designs and system-level planning.

In practice

• Separate compute-intensive and memory-bandwidth parts onto different chips.
• Utilize advanced packaging for better PPA and thermal profiles.
• Reuse qualified design libraries and interface IP.

Topics

• ASIC Design
• Chip Disaggregation
• Advanced Packaging
• AI Workloads
• System-Specific ASICs

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Hardware Engineer, AI Architect

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