Special Report

Machine Learning’s Growing Divide
Is the industry heading toward another hardware/software divide in machine learning? Both sides have different objectives.

Top Stories

Turning Down The Power
Why ultra-low power is suddenly an issue for everyone.

Predictions: Markets And Drivers
Part 1: What advancements can we expect to see in 2018, which markets will drive the industry, and what are the major challenges that have to be addressed?

Blogs

Editor in Chief Ed Sperling points to orders of magnitude performance increases in the future, in Warp Speed Ahead.

Fraunhofer EAS’ Roland Jancke contends that current design processes are not keeping up with the auto industry’s ambitious demands, in Electronic Design For Reliable Autonomous Driving.

Mentor’s Jeff Mayer observes that test points for hybrid ATPG/LBIST applications make it easier to reach the ISO 26262 standard of 90% stuck-at coverage for in-system test, in A Simple Way To Improve Automotive In-System Test.

Moortec’s Ramsay Allen notes that the breakdown of Moore’s Law means finding new ways to improve performance, in Why Pinpoint Accuracy Is Important When Monitoring Conditions On Chip.

Rambus’ Steven Woo explains the central role that memory systems play in enabling new technologies, in The Data Center In 2018 And Beyond.

Helic’s Magdy Abadir, Anand Raman and Yukari Ohno find that accurate modeling of surrounding structures is key to solving EM crosstalk issues, in Preparing For Electromagnetic Crosstalk Challenges.

Cadence’s Thomas Wong questions how cars should make moral decisions, and why it might not matter, in Self-Driving Cars And Kobayashi Maru.

Synopsys’ Andrew Elias shows how a hardware-based Root of Trust works and why it’s necessary, in Protecting Automotive Systems With A Root Of Trust.

Arm’s Prithi Ramakrishnan looks at the impact of context-aware devices with Bluetooth Low Energy on battery life, in Beacons Beckon Ubiquity In IoT Era.