Big Challenges, Changes For Debug


By Ann Steffora Mutschler & Ed Sperling Debugging a chip always has been difficult, but the problem is getting worse at 7nm and 5nm. The number of corner cases is exploding as complexity rises, and some bugs are not even on anyone's radar until well after devices are already in use by end customers. An estimated 39% of verification engineering time is spent on debugging activities the... » read more

System Bits: Nov. 28


Better absorbing materials
 University of Illinois bioengineers have taken a new look at an old tool to help characterize a class of materials called metal organic frameworks (MOFs), used to detect, purify and store gases. The team believes these could help solve some of the world's most challenging energy, environmental and pharmaceutical challenges – and even pull water molecules straigh... » read more

System Bits: Nov. 21


MIT-Lamborghini to develop electric car Members of the MIT community were recently treated to a glimpse of the future as they passed through the Stata Center courtyard as the Lamborghini Terzo Millenio (Third Millennium) was in view, which is an automobile prototype for the third millennium. [caption id="attachment_429209" align="alignnone" width="300"] Lamborghini is relying on MIT to make i... » read more

Power/Performance Bits: Nov. 7


Speeding up MRAM Researchers at UC Berkeley and UC Riverside developed an ultrafast method for electrically controlling magnetism in certain metals, which could lead to increased performance for magnetic RAM. While the nonvolatility of MRAM is a boon, speeding up the writing of a single bit of information to less than 10 nanoseconds has been a challenge. “The development of a non-volatile... » read more

Power/Performance Bits: Oct. 31


Battery material supplies Researchers at MIT, the University of California at Berkeley, and the Rochester Institute of Technology conducted an analysis of whether there are enough raw materials to support increased lithium-ion battery production, expected to grow significantly due to electric vehicles and grid-connected battery systems. They conclude that while in the near future there shou... » read more

Deep Learning Robust Grasps with Synthetic Point Clouds & Analytic Grasp Metrics (UC Berkeley)


Source: The research was the work of Jeffrey Mahler, Jacky Liang, Sherdil Niyaz, Michael Laskey, Richard Doan, Xinyu Liu, Juan Aparicio Ojea, and Ken Goldberg with support from the AUTOLAB team at UC Berkeley. Nimble-fingered robots enabled by deep learning Grabbing awkwardly shaped items that humans regularly pick up daily is not so easy for robots, as they don’t know where to apply grip... » read more

System Bits: June 13


Nimble-fingered robots enabled by deep learning Grabbing awkwardly shaped items that humans regularly pick up daily is not so easy for robots, as they don’t know where to apply grip. To overcome this, UC Berkeley researchers have a built a robot that can pick up and move unfamiliar, real-world objects with a 99% success rate. Berkeley professor Ken Goldberg, postdoctoral researcher Jeff M... » read more

RISC-V Pros And Cons


Simpler, faster, lower-power hardware with a free, open, simple instruction set architecture? While it sounds too good to be true, efforts are underway to do just that with RISC-V, the instruction-set architecture (ISA) developed by UC Berkeley engineers and now administered by a foundation. It has been known for some time that with [getkc id="74" comment="Moore's Law"] not offering the same... » read more

Speeding Up Neural Networks


Neural networking is gaining traction as the best way of collecting and moving critical data from the physical world and processing it in the digital world. Now the question is how to speed up this whole process. But it isn't a straightforward engineering challenge. Neural networking itself is in a state of almost constant flux and development, which makes it something of a moving target. Th... » read more

System Bits: March 14


Neuromorphic computing While for five decades, Moore’s law held up pretty well, today, transistors and other electronic components are so small they’re beginning to bump up against fundamental physical limits on their size, and because Moore’s law has reached its end, it’s going to take something different to meet the need for computing that is ever faster, cheaper and more efficient. ... » read more

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