tag: UC Berkeley

A New Memory Contender?
By Mark LaPedus - 02 Jan, 2018 - Comments: 1

Momentum is building for a new class of ferroelectric memories that could alter the next-generation memory landscape. Generally, ferroelectrics are associated with a memory type called ferroelectric RAMs (FRAMs). Rolled out by several vendors in the late 1990s, FRAMs are low-power, nonvolatile devices, but they are also limited to niche applications and unable to scale beyond 130nm. While... » read more

System Bits: Jan. 2
By Ann Mutschler - 02 Jan, 2018 - Comments: 0

Robots imagine their future to learn By playing with objects and then imagining how to get the task done, UC Berkeley researchers have developed a robotic learning technology that enables robots to figure out how to manipulate objects they have never encountered before. The team expects this technology could help self-driving cars anticipate future events on the road and produce more intel... » read more

Chipmakers Look To New Materials
By Jeff Dorsch - 18 Dec, 2017 - Comments: 1

Graphene, the wonder material rediscovered in 2004, and a host of other two-dimensional materials are gaining ground in manufacturing semiconductors as silicon’s usefulness begins to fade. And while there are a number of compounds in use already, such as gallium arsenide, gallium nitride, and silicon carbide, those materials generally are being confined to specific niche applications. Tran... » read more

Big Challenges, Changes For Debug
By Ann Mutschler - 04 Dec, 2017 - Comments: 1

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
By Ann Mutschler - 28 Nov, 2017 - Comments: 0

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
By Ann Mutschler - 21 Nov, 2017 - Comments: 0

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
By Jesse Allen - 07 Nov, 2017 - Comments: 0

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
By Jesse Allen - 31 Oct, 2017 - Comments: 0

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)
By Technical Paper Link - 28 Jun, 2017 - Comments: 0

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
By Ann Mutschler - 13 Jun, 2017 - Comments: 0

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

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