tag: neural networks

The Future Of AI Is In Materials
By Sundeep Bajikar - 09 Jan, 2018 - Comments: 0

I had the pleasure of hosting an eye-opening presentation and Q&A with Dr. Jeff Welser of IBM at a recent Applied Materials technical event in San Francisco. Dr. Welser is Vice President and Director of IBM Research's Almaden lab in San Jose. He made the case that the future of hardware is AI. At Applied Materials we believe that advanced materials engineering holds the keys to unlocking... » read more

The Week In Review: Design
By Jesse Allen - 22 Dec, 2017 - Comments: 0

Altium released the latest version of its PCB design suite. Improvements include a new interface and an upgrade to 64-bit architecture combined with multi-threaded task optimizations. Other additions include a new BoM rule checker and length tuning and pin-swapping in the user-guided routing engine. Creonic announced a new line of IP for 5G forward error correction. The product line covers t... » read more

System Bits: Dec. 12
By Ann Mutschler - 12 Dec, 2017 - Comments: 0

Increasing performance scaling with packageless processors Demand for increasing performance is far outpacing the capability of traditional methods for performance scaling. Disruptive solutions are needed to advance beyond incremental improvements. Traditionally, processors reside inside packages to enable PCB-based integration. However, a team of researchers from the Department of Electrical ... » read more

Power/Performance Bits: Nov 28
By Linda Christensen - 28 Nov, 2017 - Comments: 0

Deep learning to detect nuclear reactor cracks Inspecting nuclear power plant components for cracks is critical to preventing leaks, as well as to control in maintenance costs. But the current vision-based crack detection approaches are not very effective. Moreover, they are prone to human error, which in the case of nuclear power can be disastrous. To address this problem, Purdue Universit... » read more

Software Framework Requirements For Embedded Vision
By Gordon Cooper - 09 Nov, 2017 - Comments: 0

Deep learning techniques such as convolutional neural networks (CNN) have significantly increased the accuracy—and therefore the adoption rate—of embedded vision for embedded systems. Starting with AlexNet’s win in the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC), deep learning has changed the market by drastically reducing the error rates for image classification and d... » read more

How Neural Networks Think (MIT)
By Technical Paper Link - 18 Oct, 2017 - Comments: 0

Source: MIT’s Computer Science and Artificial Intelligence Laboratory, David Alvarez-Melis and Tommi S. Jaakkola Technical paper link MIT article General-purpose neural net training Artificial-intelligence research has been transformed by machine-learning systems called neural networks, which learn how to perform tasks by analyzing huge volumes of training data, reminded MIT research... » read more

Terminology Beyond von Neumann
By Katherine Derbyshire - 21 Sep, 2017 - Comments: 0

Neural networks. Neuromorphic computing. Non-von Neumann architectures. As I’ve been researching my series on neuromorphic computing, I’ve encountered a lot of new terminology. It hasn’t always been easy to figure out exactly what’s being discussed. This explainer attempts to both clarify the terms used in my own articles and to help others sort through the rapidly growing literature in... » read more

Computer Vision Powers Startups, Bleeding Edge Processes
By Ann Mutschler - 10 Aug, 2017 - Comments: 0

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 CNNs To Speed Up Systems
By Ann Mutschler - 10 Aug, 2017 - Comments: 0

Convolutional neural networks (CNNs) are becoming one of the key differentiators in system performance, reversing a decades-old trend that equated speed with processor clock frequencies, the number of transistors, and the instruction set architecture. Even with today's smartphones and PCs, it's difficult for users to differentiate between processors with 6, 8 or 16 cores. But as the amount o... » read more

The Evolution Of Deep Learning For ADAS Applications
By Gordon Cooper - 08 Jun, 2017 - Comments: 0

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

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