Data Centers Turn To New Memories


DRAM extensions and alternatives are starting to show up inside of data centers as the volume of data being processed, stored and accessed continues to skyrocket. This is having a big impact on the architecture of data centers, where the goal now is to move processing much closer to the data and to reduce latency everywhere. Memory has always been a key piece of the Von Neumann compute archi... » read more

Tools To Design CNNs


Convolutional neural networks are becoming a mainstay in machine learning and artificial intelligence, allowing a network of distributed sensors to collect data and send them to a central brain for processing. This is a relatively simple idea in comparison to today's technology, and the idea of the [getkc id="261" kc_name="convolutional neural network"] has been around for some time. But bui... » read more

What’s New At Hot Chips


By Jeff Dorsch & Ed Sperling Machine learning, artificial intelligence and neuromorphic computing took center stage at Hot Chips 2017 this week, a significant change from years past where the focus was on architectures that addressed improvements in speed and performance for standard compute problems. What is clear, given the focus of presentations, is that the bleeding edge of comput... » read more

What Is Spin Torque MRAM?


The memory market is going in several different directions at once. On one front, the traditional memory types, such as DRAM and flash, remain the workhorse technologies. Then, several vendors are readying the next-generation memory types. As part of an ongoing series, Semiconductor Engineering will explore where the new and traditional memory technologies are heading. For this segment, P... » read more

Computer Vision Powers Startups, Bleeding Edge Processes


You can’t turn around these days without walking into a convolutional neural network…..oh wait, maybe not yet, but sometime in the not-too-distant future, we’ll be riding in vehicles controlled by them. While not a new concept, CNNs are finally making the big time, as evidenced by a significant upswell in startup activity, tracked by Chris Rowen, CEO of Cognite Ventures. According to h... » read more

Using Machine Learning In EDA


Machine learning is beginning to have an impact on the EDA tools business, cutting the cost of designs by allowing tools to suggest solutions to common problems that would take design teams weeks or even months to work through. This reduces the cost of designs. It also potentially expands the market for EDA tools, opening the door to even new design starts and more chips from more compan... » read more

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

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