Data Leakage In Heterogeneous Systems


Semiconductor Engineering sat down with Paul Chou, senior director of security architecture at NVIDIA, to discuss data leakage in heterogeneous designs. What follows are excerpts of that one-on-one interview, which was held in front of a live audience at the Hardwear.io conference. SE: We think about hardware in terms of a chip, but increasingly there is data moving through different systems... » read more

How Much AI Is Really Needed?


Tensor Core GPUs have created a generative AI model gold rush. Whether it’s helping students with math homework, planning a vacation, or learning to prepare a six-course meal, generative AI is ready with answers. But that's only one aspect of AI, and not every application requires it. AI — now an all-inclusive term, referring to the process of using algorithms to learn, predict, and make... » read more

Making Sensors More Reliable


Experts at the Table: Semiconductor Engineering sat down to talk about the latest issues in sensors with Prakash Madhvapathy, director of product marketing, Tensilica audio/voice DSPs group at Cadence; Kevin Hughes, senior product manager for MEMS sensors at Infineon; and Matthew Hogan, product management director at Siemens EDA. What follows are excerpts of that conversation. [L-R] Kevin ... » read more

Gearing Up For Level 4 Vehicles


More autonomous features are being added into high-end vehicles, but getting to full autonomy will likely take years more effort, a slew of new technologies — some of which are not in use today, and some of which involve infrastructure outside the vehicle — along with sufficient volume to bring the cost of these combined capabilities down to an affordable price point. In the meantime, ma... » read more

Quantum Plus AI Widens Cyberattack Threat Concerns


Quantum computing promises revolutionary changes to the computing paradigm that the semiconductor industry has operated under for decades, but it also raises the prospect of widespread cybersecurity threats. Quantum computing cyberattacks will occur millions of times faster than any assault conventional computing can muster. And while quantum computing is in an early stage of development, ex... » read more

The Threat Of Supply Chain Insecurity


Concerns about counterfeit chips are growing as more chips are deployed in safety- and mission-critical applications, prompting better traceability and new and inexpensive solutions that can determine if chips are new or used. But some counterfeit chips still slip through, and the problem gets worse wherever there are shortages. Estimates vary widely for how much counterfeiting costs in term... » read more

Designing Vehicles Virtually


The shift toward software-defined vehicles (SDVs), electric vehicles (EVs), and ultimately autonomous vehicles (AVs) is proving the value and exposing the weaknesses in simulating individual components and complete vehicles. The ability to model this intensely complex maze of real-world interactions and possible scenarios is improving, and it's happening faster than comparable road-testing o... » read more

Setting Standards For The Chip Industry


For all the advances in semiconductor design, and the astonishing scales on which the industry now works, when it comes to standards committee meetings, not much has changed. Advice from a 91-year-old retired engineer can sound surprisingly like advice from those active today. Standards were then, and continue to be, a mix of technical compromises and corporate politics, as well as passionate a... » read more

Automotive Complexity, Supply Chain Strength Demands Tech Collaboration


The automotive supply chain is becoming more complex and collaborative, changing longstanding relationships between automakers and their suppliers in ways that would have seemed unimaginable even a couple of years ago. Rather than just developing parts for a tightly defined specification, suppliers are taking an increasingly active role in determining how various technologies are combined, w... » read more

AI Transformer Models Enable Machine Vision Object Detection


The object detection required for machine vision applications such as autonomous driving, smart manufacturing, and surveillance applications depends on AI modeling. The goal now is to improve the models and simplify their development. Over the years, many AI models have been introduced, including YOLO, Faster R-CNN, Mask R-CNN, RetinaNet, and others, to detect images or video signals, interp... » read more

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