Author's Latest Posts


AI Is Rewriting The IP Playbook


Key Takeaways:  AI is reshaping the entire IP lifecycle, from creation and verification to discovery, licensing, and support.  Fast-changing AI models are making flexible IP, robust toolchains, and faster deployment essential.  Human expertise remains critical for reviewing, validating, and governing AI-assisted IP development.  AI is becoming part of the everyday work o... » read more

Defending Against AI-Enabled Data Fusion


Key Takeaways:  By fusing vast amounts of white-, gray-, and black-market data, attackers can build a digital twin of a person and their environment, making targeted attacks far easier.  The intersection of AI and cybersecurity is the data itself. Trustworthy fusion depends on authenticated, integrity‑checked inputs and verifiable, attributable AI outputs.   Defending a... » read more

Observability Is A Missing Layer In AI-Era Chiplet Design


Key Takeaways: In chiplet-based architectures, observability must be designed as a fabric-aligned, cross-die telemetry plane so architects can correlate traffic, latency, congestion, and fault behavior across package boundaries without losing system context. AI can extract value from high-volume silicon telemetry only when the architecture provides consistent instrumentation, near-senso... » read more

Rethinking Chip Verification


Key Takeaways: AI and modern tools are easing traditional verification pain, but they're not addressing the underlying bottleneck in complex designs. Work is underway to create a golden, unambiguous spec above RTL, tracing requirements from spec to implementation to verification and checking for gaps, conflicts, and inconsistencies across levels and blocks, often with AI help. Tool c... » read more

Designing Chips That Can Explain Themselves


Key Takeaways: On-die telemetry gives architects a path to replace worst-case design margin with measured silicon behavior, improving PPA without compromising resilience. As monitor density and control-loop speed increase, observability must be architected hierarchically across local hardware response, on-die processing, and fleet-level learning. The real payoff is architectural: str... » read more

Agentic AI Is Changing Data Center Architectures


Key Takeaways: The rise of agentic AI is shifting data centers from GPU-centric number crunching to CPU-driven orchestration, where managing long-running reasoning loops and context is just as important as raw compute. Integrating CPUs, GPUs, and stacked memory into tightly coupled multi-die architectures with varying workloads makes it much harder to ensure they will be reliable and ef... » read more

Keeping Security Algorithms Current Is Getting Harder


Key Takeaways: Keeping security algorithms current is now a lifecycle challenge that spans chip design, manufacturing, deployment, and long-term maintenance across the supply chain. To stay ahead of emerging threats — especially post-quantum risks — hardware must be built with cryptographic agility, secure roots of trust, and reliable update mechanisms from the start. The bigge... » read more

Observability Is Essential For Modern Silicon


Experts At The Table: In-silicon observability — also known as on-die or on-chip visibility — is becoming increasingly important for managing the performance, reliability, and security of today’s high-performance systems. Semiconductor Engineering sat down to discuss this with Andy Nightingale, vice president of product management and marketing at Arteris; Nandan Nayampally, chief commerc... » read more

Chiplets Need A New Workflow


Key Takeaways: Chiplet design turns semiconductor development into a system-level problem, requiring coordinated workflows across design, packaging, verification, test, and reliability. Successful chiplet workflows must handle multi-physics challenges — especially thermal, mechanical, power, and signal integrity — early enough to reduce costly failures before assembly and tape-out. ... » read more

Building AI Without Guardrails


Key Takeaways: AI governance is broadly recognized as essential, but today it remains fragmented, largely aspirational, and lacking enforceable mechanisms for accountability, runtime assurance, and global interoperability. Because AI innovation is advancing too quickly for governments or standards bodies to keep pace, practical AI governance is most likely to emerge first from high‑ri... » read more

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