2026 Predictions: System-Level Design, AI-Native Workflows, and the Rise of Multi-Die Compute Fabrics

The semiconductor industry enters 2026 amid a fundamental transformation as AI continues to expand faster than any previous compute wave. SoC design teams are confronting a level of complexity that is forcing a shift toward system-level thinking that prioritizes scalable compute and memory fabrics over traditional SoC approaches.

Across the industry, AI continues to fuel a historic growth cycle, with data-processing applications on track to surpass 50% of total semiconductor revenue for the first time. But beneath that macro trend lies a more fundamental transformation of how advanced systems are architected, integrated, and verified.

Arteris experts predict that as we move forward in 2026, the key breakthroughs and the biggest risks will be tied not to individual IP blocks, but to the data-movement infrastructure across entire systems. Here are some of our key predictions on what to expect industry-wide in the coming year.

1. System-Level Architecture Replaces SoC-Centric Design

By 2026, design teams increasingly prioritize scalable compute and memory fabrics over isolated SoCs. Architectures now incorporate large numbers of initiators, multiple memory tiers, workload-specific accelerators, and 2.5D and 3D integration with high-density die-to-die connectivity.

With this shift comes a new architectural baseline: the interconnect, last-level cache, and die-to-die fabric are no longer plumbing, they are the system. This means:

• Teams must now examine behavior at the system level, not at the block level.
• Traffic patterns, coherency interactions, and cross-die latency variations form the real determinants of performance and predictability.
• Improving first-silicon outcomes requires validating full-system behavior, including memory hierarchy interactions, rather than checking only RTL correctness.

The designs that succeed in 2026 will be those that adopt structured, pre-verified fabrics capable of spanning dies and packaging formats without introducing fragility or bespoke, unscalable workarounds.

2. AI Becomes Integral to Architecture, Verification, and Physical Design

The exponential rise of AI is not limited to end applications but is reshaping how chips are designed. Early productivity gains from code generation and test-bench creation are giving way to deeper changes in architecture exploration, NoC topology generation, debugging, and physical design.

In 2026, AI will increasingly suggest architectural variants optimized for system behavior, evaluate PPA trade-offs for 2.5D/3D layouts, predict congestion and interconnect timing interactions, and accelerate physical design using reinforcement learning approaches.

However, trust and explainability remain critical. Teams must understand why AI-driven recommendations work, ensuring scalable adoption across large SoC and multi-die programs.

3. AI-Driven Exploration Accelerates but Requires Trust and Data Quality

In addition to chip design, AI will increasingly guide architectural exploration by identifying optimal PPA configurations in design spaces too large for traditional heuristics, as with FlexGen smart NoC. Machine learning models can accelerate these trade-off analyses and quickly identify optimal configurations, reducing manual iterations and shortening time-to-market.

However, challenges remain. Integrating AI into existing design flows requires trust and explainability, as designers need transparency in automated decisions. Additionally, ensuring scalability for large SoC designs and managing data quality for training models will be critical hurdles to overcome in 2026.

4. Integration for 2.5D/3D Expands, Elevating the Importance of Interconnect Topology

As packaging technologies mature and compute requirements spike, 2.5D and 3D architectures are expanding well beyond HPC and AI accelerators. These layouts multiply design complexity through interposer routing constraints, thermal interactions, power delivery, and heterogeneous IP and memory technologies.

AI can help navigate this multidimensional space, but it cannot eliminate the architectural truth: multi-die systems are ultimately bottlenecked by data movement. Architects must balance coherency domains, partition compute clusters, and design memory subsystems with an awareness of traffic patterns and latency sensitivity.

5. Chiplets Continue Expanding but Remain Mostly in Closed Ecosystems

Chiplets are poised for broader adoption in 2026, but the vision of a fluid, open chiplet marketplace remains distant. Integration risk, rather than technology availability, remains the limiting factor.

Most chiplet progress will occur within single companies or tight multi-party collaborations, taking the form of modular, composable subsystems. To scale, these architectures must be built on reliable fabrics that integrate NoC, LLC, PCIe/CXL, and die-to-die connectivity into cohesive, verifiable topologies.

6. RISC-V and Open IP Ecosystems Advance Due to Interconnect Scalability

Growth in RISC-V and open-source IP will hinge on reliable, scalable interconnects and memory systems that seamlessly map across various packaging formats. In short, multi-die compute fabrics that tightly integrate the NoC, last-level cache, and PCIe/CXL infrastructure will become the essential foundation for advanced system design.

As open architectures gain traction, their ability to scale hinges on the interconnect. Diverse cores, accelerators, and chiplets require coherent and non-coherent traffic coordination, flexible memory models, and reliable cross-die interaction. Instead of ISA features, optimized and proven system fabrics will determine which designs succeed.

7. Industry Macro Forces Reinforce the Need for System-Level Fabrics

The broader semiconductor landscape, including expansion of AI infrastructure, rapid HPC and memory growth, and the dominance of data-center spending, further accelerates the need for scalable system fabrics. This places extraordinary pressure on interconnects, memory subsystems, and die-to-die bandwidth to support AI’s insatiable appetite for data.

Conclusion: The Future Belongs to Compute Fabrics, Not Individual SoCs

The year ahead will redefine how the industry thinks about scale, performance, and system reliability. Across all 2026 trends in AI acceleration, chiplets, RISC-V growth, advanced packaging, and automotive evolution, one truth stands out: the ability to move data efficiently, predictably, and at scale now determines system performance.

For the next generation of compute systems, the interconnect is not a supporting technology. It is the foundation. Organizations that embrace NoC interconnect-centric architecture, AI-accelerated exploration, and full-system verification will lead the next decade of semiconductor innovation.

By delivering scalable NoC interconnect fabrics, coherent subsystems, and automation technology that unifies system-level design and verification, Arteris enables data to move exactly where and when it’s needed. As architectures grow more distributed and AI workload-driven, Arteris advanced products will help ensure that data movement becomes a catalyst for semiconductor innovation, and not a constraint.