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Blog Review: March 17

More auto sensors; bootstrapping; initiating structures; neural net labels.

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Synopsys’ Chris Clark considers the growing number of automotive sensors and the cost/performance tradeoffs between edge computing capability, sensor fusion, sensor degradation, monitoring, and the maintenance of the software over the lifespan of a vehicle.

Cadence’s Paul McLellan checks out how the process of loading the bootstrap into memory has changed over the years, from hand-entered on binary switches to modern servers.

Siemens EDA’s Colin Walls explains how to use C to initiate structures and complex objects such as arrays.

Arm’s Axel Berg explores why it can be advantageous to use multiple labels or neural network learning, particularly when problems don’t fit neatly into the classification or regression categories.

Ansys’ Scott Nyberg finds out how a civil engineering professor at Eindhoven University of Technology is using fluid simulation to assess and improve the safety of indoor gyms and exercise facilities.

Applied Materials’ Ortal Yesodi argues that the way to keep up with the growing challenge of separating noise from defects when using advanced optical inspection systems is by collecting and analyzing as much data as possible.

SEMI’s Gity Samadi checks out four projects using flexible electronics for wearables, medical devices and precision agriculture.

ON Semiconductor’s Danny Scheffer shows how the Mars Perseverance rover used commercial off-the-shelf image sensors for the cameras that helped it determine, autonomously, exactly where to land.

Silicon Labs’ Emmanuel Sambuis considers the security and privacy challenges surrounding connected medical devices and how new security features in BLE plus roots of trust and over-the-air updates can help.

Plus, check out the blogs highlighted in the latest Low Power-High Performance newsletter:

Ansys’ Marc Swinnen digs into how 3D design will force IC design teams to face new physics domains and challenges.

Arm’s Chet Babla looks at some key trends — from consolidated processing power to smarter batteries — to watch in the coming years.

Infineon’s Peter Friedrichs examines similarities and differences between SiC and silicon.

Siemens EDA’s Kurt Takara demonstrates how to ensure that data transfer between power domains is not corrupted by metastability.

Synopsys’ Aveek Sarkar contends that increasing complexity means designs containing analog and digital components need to be analyzed as one system.

Cadence’s Paul McLellan zeroes in on what type of high-speed interface to use when connecting chiplets.

Rambus’ Frank Ferro observes that 2.5D memory provides the bandwidth, capacity, and power efficiency needed for AI training, but comes with added complexity.

Calibra’s Jan Willis points to updates on using curvilinear shapes to increase process windows for advanced memory.

Valtrix Systems’ Shubhodeep Roy Choudhury and Imperas’ Lee Moore explain how RISC-V verification ecosystems support flexibility in approaching a custom processor design.



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