Special Report
Power Is Limiting Machine Learning Deployments
Rollouts are constrained by the amount of power consumed, and that may get worse before it gets better.
Top Stories
Low-Power Design Becomes Even More Complex
New markets, technologies and tradeoffs that span multiple different disciplines are turning this into an increasingly difficult team effort.
Will In-Memory Processing Work?
Changes that sidestep von Neumann architecture could be key to low-power ML hardware.
Blogs
Editor In Chief Ed Sperling argues that just because millimeter wave technology is built into a handset doesn’t mean it will be useful, in Where 5G Works, And Where It Doesn’t.
Mentor’s Progyna Khondkar urges engineers to become more accurate, productive, and consistent by understanding the inherent features of UPF commands and options, in Empowering UPF Commands With Effective Elements Lists.
Synopsys’ Johannes Stahl stresses the need for making accurate estimates of power consumption now that most chip designs employ low-power design techniques, in Accurate Power Analysis Using Real Software Workloads.
Rambus’ Frank Ferro looks at GDDR and how to get enough bandwidth to meet the demands of ever more sophisticated AI/ML applications, in GDDR Accelerates Artificial Intelligence And Machine Learning.
Adesto’s Jen Bernier-Santarini explains how a fermentation tank networked communication system is used to ensure the optimal temperature for brewing and winemaking, in Using Technology To Improve Beer And Wine.
