Blog Review: Jan. 11

Choosing a CFD mesh; developing for software-defined vehicles; digital twins; co-packaged optics for lidar.


Cadence’s Veena Parthan explains why in CFD, understanding the consequences of choices regarding the computational mesh is essential for generating high-fidelity simulation results.

Synopsys’ Chris Clark shares key considerations and questions to factor in when developing solutions for software-defined vehicles that must meet safety, security, reliability, and quality standards.

Siemens EDA’s Jake Wiltgen finds that the adoption of digital twins by systems integrators and car manufacturers has new implications and challenges for semiconductor suppliers.

Ansys’ Sanjay Gangadhara finds co-packaged optics may be adopted in new and emerging technology spaces like automotive lidar before data centers.

SEMI’s Cassandra Melvin shares highlights from SEMICON Europa, including collaborative research and development, the role semiconductors in greener and more trusted applications, and challenges and opportunities in manufacturing and global supply chain optimization.

Renesas’ Akihiro Ooshima considers how design standardization of an MCU can reduce not only the component cost but also the maintenance workload in the future.

In a podcast, Arm’s Geof Wheelwright and Dermot O’Driscoll discuss the company’s infrastructure roadmap for data center, cloud, and HPC.

Plus, check out the blogs featured in the latest Automotive, Security & Pervasive Computing and Test, Measurement & Analytics newsletters:

Rambus’ Bart Stevens show how to ensure a security architecture does not impede the intended use of the device while still protecting assets.

Flex Logix’s Geoff Tate looks at how to implement programmable FIR filters by hardening the data path while keeping the control path in eFPGAs.

Synopsys’ Laureano Carrasco examines how a bug in a datapath could result in chip misbehavior just as surely as an error in a state machine.

Cadence’s Ben Gu points to a big opportunity for carmakers to reimagine and redefine the entire automotive experience.

Arteris’ Frank Schirrmeister explains why better prediction of physical implementation effects leads to faster results for the final layout.

Riscure’s Nicole Fern warns that printers are attractive targets because they are often privy to sensitive data and reside inside corporate networks.

Siemens’ Jacob Wiltgen zeroes in on how to protect an embedded hardware security module against random failures.

Infineon’s Danie Schneider describes how to avoid unplanned downtime and operational disruption in building equipment.

Synopsys’ Gordon Cooper details a new class of neural network models that are opening the door to full visual perception.

Teradyne’s Natalian Z. Der explains why testing products in a manner closely matching the end use improves quality and shortens time to market.

Advantest’s Sonny Banwari and Matthias Sauer look at establishing an ecosystem for data-driven ML test offerings.

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