Planes, Birdhouses And Image Recognition


My recent blog post on the limits of neuromorphic computing took an optimistic view: even neuromorphic systems that are relatively crude by the standards of biological brains can still find commercially important applications. A few days after I finished it, I was reminded that the pessimists are not wrong when a friend of mine shared this image. Fig. 1: Trover Gourds in purple martin nest... » read more

Machine Learning Popularity Grows


Machine learning and deep learning are showing a sharp growth trajectory in many industries. Even the semiconductor industry, which generally has resisted this technology, is starting to changing its tune. Both [getkc id="305" kc_name="machine learning"] (ML) and deep learning (DL) have been successfully used for image recognition in autonomous driving, speech recognition in natural langua... » read more

System Bits: July 11


An algorithm to diagnose heart arrhythmias with cardiologist-level accuracy To speed diagnosis and improve treatment for people in rural locations, Stanford University researchers have developed a deep learning algorithm can diagnose 14 types of heart rhythm defects better than cardiologists. The algorithm can sift through hours of heart rhythm data generated by some wearable monitors to f... » read more

Machine Learning Meets IC Design


Machine Learning (ML) is one of the hot buzzwords these days, but even though EDA deals with big-data types of issues it has not made much progress incorporating ML techniques into EDA tools. Many EDA problems and solutions are statistical in nature, which would suggest a natural fit. So why is it so slow to adopt machine learning technology, while other technology areas such as vision recog... » 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

The Evolution Of Deep Learning For ADAS Applications


Embedded vision solutions will be a key enabler for making automobiles fully autonomous. Giving an automobile a set of eyes – in the form of multiple cameras and image sensors – is a first step, but it also will be critical for the automobile to interpret content from those images and react accordingly. To accomplish this, embedded vision processors must be hardware optimized for performanc... » read more

System Bits: June 6


Silicon nanosheet-based builds 5nm transistor To enable the manufacturing of 5nm chips, IBM, GLOBALFOUNDRIES, Samsung, and equipment suppliers have developed what they say is an industry-first process to build 5nm silicon nanosheet transistors. This development comes less than two years since developing a 7nm test node chip with 20 billion transistors. Now, they’ve paved the way for 30 billi... » read more

What’s Next In Neural Networking?


Faster chips, more affordable storage, and open libraries are giving neural network new momentum, and companies are now in the process of figuring out how to optimize it across a variety of markets. The roots of neural networking stretch back to the late 1940s with Claude Shannon’s Information Theory, but until several years ago this technology made relatively slow progress. The rush towar... » read more

System Bits: April 18


RISC-V errors Princeton University researchers have discovered a series of errors in the RISC-V instruction specification that now are leading to changes in the new system, which seeks to facilitate open-source design for computer chips. In testing a technique they created for analyzing computer memory use, the team found over 100 errors involving incorrect orderings in the storage and retr... » read more

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