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Ann Mutschler

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Ann Mutschler is senior executive editor at Semiconductor Engineering.

Author's Latest Posts

Designing Chips That Can Explain Themselves
By Ann Mutschler - 17 Jun, 2026 - Comments: 1

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
By Ann Mutschler - 11 Jun, 2026 - Comments: 0

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
By Ann Mutschler - 04 Jun, 2026 - Comments: 0

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
By Ann Mutschler - 28 May, 2026 - Comments: 0

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
By Ann Mutschler - 14 May, 2026 - Comments: 0

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
By Ann Mutschler - 07 May, 2026 - Comments: 0

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

Designing Chips In The Context Of Rapidly Evolving AI
By Ann Mutschler - 04 May, 2026 - Comments: 0

Key Takeaways: Agentic edge AI drives long-lived, tool-mediated loops with variable demands for compute, tokens, and memory. Edge PPA is dominated by memory hierarchy and data movement, forcing tight feature triage and robust RAS. Rapid model churn (multimodal, MoE, new formats) requires programmable, headroom-rich compute, interconnect, and runtime. Experts At The Table: Ch... » read more

Foundry Capacity Is Limiting Who Competes At Leading Edge Nodes
By Ann Mutschler - 30 Apr, 2026 - Comments: 0

Key Takeaways: Leading-edge node access is increasingly reserved for hyperscalers, squeezing smaller chip developers. Chiplets and advanced packaging offer a path forward, but raise cost, complexity, and risk — especially for smaller teams. Chip architecture is now driven as much by capacity, yield, and economics as by technical goals. The benefits of device scaling are sl... » read more

Can Edge AI Keep Up?
By Ann Mutschler - 23 Apr, 2026 - Comments: 0

Key Takeaways: Model development is outpacing silicon design cycles, so edge AI architectures must prioritize adaptability. The required cadence for model updates is highly application-dependent and is closely tied to product lifetime and operational risk. Adaptability can conflict with power, performance, and area targets, so effective heterogeneous architectures and robust softwa... » read more

Fast Isn’t Fast Enough: Redefining Metrics for Edge AI
By Ann Mutschler - 09 Apr, 2026 - Comments: 0

Key Takeaways: Edge AI performance is about low latency and power efficiency, not peak TOPS. Memory bandwidth and data movement now limit edge AI more than compute. Successful edge AI requires balanced hardware, software, and fast model updates. Experts At The Table: Today’s chip architect must contend with multiple factors when architecting AI processors for fast and effi... » read more

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