AI Workloads at the Edge: Ensuring Performance, Privacy, and Security


Experts At The Table: Semiconductor Engineering gathered a group of experts to discuss why some AI workloads are better suited for on-device processing to achieve consistent performance, avoid network connectivity issues, reduce cloud computing costs, and ensure privacy. The panel included Frank Ferro, group director in the Silicon Solutions Group at Cadence; Eduardo Montanez, vice president a... » read more

Zero-Trust Data Sharing Architectures Redefining Chip Manufacturing


Real-time security clearances are becoming increasingly common in the manufacturing of advanced-node semiconductors, where data sharing is both essential and a potential security threat. Data security is a well-known issue in semiconductor manufacturing, but much of it is based on an outdated approach. In its place, zero-trust architectures [1] are now a requirement for new equipment and ins... » read more

Securing The Design Journey


In automotive, security, and pervasive computing, the stakes of failure have never been higher. Functional safety, security compliance, long product lifecycles, and system resilience are no longer differentiators — they are baseline requirements. Yet many semiconductor and system companies are still relying on an outdated engagement model built around static datasheets, fragmented tools, and ... » read more

TEE.fail: When Your Security System Leaves the Window Open


Let’s talk about a cybersecurity attack that’s been making waves: TEE.fail. TEE stands for Trusted Execution Environment. Sounds reassuring, right? But here’s the kicker: exactly what a TEE is, and what it’s supposed to guarantee, is surprisingly unclear. TEEs have been around for about a decade, but as with many things in security, the rules are more like guidelines. You might think, �... » read more

What The EU Cyber Resilience Act Means For Digital Product Makers


The EU Cyber Resilience Act (CRA) is set to become a defining regulation for all manufacturers and developers of digital products that touch the EU market. It introduces strict requirements for cybersecurity practices, risk management, and compliance procedures, affecting a wide range of stakeholders from software developers to hardware vendors. This article unpacks what the CRA is, who it af... » read more

Small Language Models Create New Security Risks


The rollout of edge AI is creating new security risks due to a mix of small language models (SLMs), their integration into increasingly complex hardware, and the behavior and interactions of both over time. AI data centers still garner the most attention due to massive investments and an ongoing flood of deals and acquisitions, but the edge is quietly starting to take shape for several reaso... » read more

Harnessing Silicon Lifecycle Management For Chip Security


Silicon lifecycle management is starting to be used in ways that extend well beyond its original mission of ensuring a chip functions to spec throughout its expected lifetime. While tracking aging effects and component failures are still important, the technology also is being deployed to proactively monitor, authenticate, and respond to potential threats in real-time. In fact, not applying ... » read more

Optimizing AI Workloads For Edge Computing


Experts At The Table: Semiconductor Engineering gathered a group of experts to discuss how some AI workloads are better suited for on-device processing to achieve consistent performance, avoid network connectivity issues, reduce cloud computing costs, and ensure privacy. The panel included Frank Ferro, group director in the Silicon Solutions Group at Cadence; Eduardo Montanez, vice president an... » read more

The Real-World Impact Of Silicon Lifecycle Management On Chip Architectures


Silicon lifecycle management (SLM) is transforming chip architectures, empowering designers to build smarter, more resilient, and secure semiconductor devices by leveraging data from manufacturing to end of life in the field. That data can be used to improve future designs, reduce margin, and continuously optimize performance and power efficiency throughout a chip's lifetime. Moreover, under... » read more

Confidential Computing To Secure AI Workloads


Artificial Intelligence (AI), data analytics, and high-performance computing (HPC) are transforming industries such as healthcare, finance, and manufacturing. These workloads rely on distributed systems managing massive datasets with high reliability. As computational demand grows, so does the need for end-to-end data protection. Traditional security addresses Data at Rest (DAR) and Data in ... » read more

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