Design Verification of Multi-Chiplet AI Accelerators: Challenges and Solutions

Abstract

The verification of multi-chiplet AI accelerators represents one of the most formidable challenges in contemporary semiconductor design, driven by the industry's transition from monolithic architectures to heterogeneous integration strategies that combine multiple specialized dies within a single package. This paradigm shift addresses fundamental limitations in Moore's Law scaling by enabling the integration of chiplets fabricated on different process nodes, optimized for specific functions such as computation, memory management, and input-output operations. However, this architectural evolution introduces unprecedented verification complexity stemming from the integration of billions of transistors across multiple dies, each containing intricate intellectual property blocks and subsystems that must communicate through sophisticated die-to-die protocols. The verification process must address not only functional correctness but also critical non-functional properties, including thermal management across stacked dies, signal integrity through vertical interconnects, power delivery network integrity, and timing predictability across asynchronous clock domains. Traditional monolithic verification approaches prove inadequate for these distributed systems, necessitating advanced methodologies that combine hardware-accelerated emulation platforms, distributed simulation techniques employing virtual channels, formal verification methods for protocol compliance, and intelligent clock gating strategies. Despite these sophisticated approaches, significant challenges persist, including substantial infrastructure costs, extended debugging cycles for distributed simulations that can span days or weeks, a combinatorial explosion of the verification space, and the complexity of achieving adequate coverage across all chiplets and operating modes. The semiconductor industry is responding with innovative solutions, including hybrid verification frameworks that strategically combine multiple methodologies, standardized chiplet interfaces that enable reusable verification components, advanced debugging tools with unified cross-chiplet waveform databases, and emerging artificial intelligence-driven techniques for intelligent testbench generation, coverage closure prediction, and verification planning optimization. These developments, while promising enhanced productivity and effectiveness, underscore that successful multi-chiplet verification requires not only technological innovation but also substantial capital investment, specialized expertise, and close collaboration between design and verification teams to deliver high-quality, bug-free AI accelerators that meet stringent performance and reliability requirements.