Ann Mutschler is the Executive Editor at EDA. She focuses on themes such as AI and machine learning, exploring topics like edge computing, cloud computing, and autonomous systems, as well as industrial systems design. Ann's insights and expertise have been featured in Semiconductor Engineering.
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Ann Mutschler's coverage primarily focuses on the semiconductor industry, chip design, and emerging technologies such as AI/ML/DL. She seems to be particularly interested in technical aspects of these industries, including processor design, verification IP, thermal impacts assessment, and glitch power issues at advanced nodes.
Given her focus on industry-specific content and press releases related to the semiconductor field, she would likely be interested in receiving pitches from professionals who can provide deep insights into cutting-edge developments within this sector. These could include experts working with RISC-V architecture or those specializing in AI accelerator architectures.
As Ann doesn't have a specific geographic focus but covers topics relevant to international technology development trends within the semiconductor industry.
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Whether caused by cosmic radiation, voltage glitches, or adversarial attacks, bit flips threaten data integrity, safety critical operation, and the foundations of hardware security.
By: Ann Mutschler
Post-quantum cryptography emerges as top concern, followed by AI and automotive complexity.
Why latency guarantees, memory movement, power budgets, and rapid model deployment now matter more than raw TOPS.
As models evolve faster than silicon cycles, experts weigh how much adaptability architects can afford without sacrificing power, area, or efficiency.
By: Ann Mutschler
The inability to access leading-edge process nodes has created opportunities for small and midsize chip developers in multi-die design, but it requires some sophisticated architectural design tradeoffs.
Long‑running agents, tool-calling LLMs, and multimodal chaos are rewriting edge compute rules, and making chip design more challenging.
The semiconductor ecosystem is wrestling with fragmented standards, IP exposure, and the urgent need for runtime assurance.
Multi-die assemblies are facing full system-level challenges, but engineering teams need coordinated and repeatable ways to identify risks early and scale reliably.
What on-die visibility reveals, and why it's especially important for AI, automotive, aerospace, and advanced packaging.
As threats evolve faster, protecting security algorithms from design through manufacturing and across the supply chain is becoming more difficult.
