How Neural Networks Think (MIT)


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


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


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


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


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

Safety Plus Security: A New Challenge


Nobody has ever integrated safety or security features into their design just because they felt like it. Usually, successive high-profile attacks are needed to even get an industry's attention. And after that, it's not always clear how to best implement solutions or what the tradeoffs are between cost, performance, and risk versus benefit. Putting safety and security in the same basket is a ... » read more

Tech Talk: Neural Networks


Megha Daga, senior technical marketing manager at Cadence, talks with Semiconductor Engineering about convolutional neural networks, including the bandwidth and compute challenges associated with them. » read more

The Week In Review: Design


Imagination has put the MIPS embedded processor and Ensigma mobile connectivity groups up for sale, refocusing on graphics after last month's announcement that Apple would no longer use the company's GPU IP. Imagination also began formal dispute resolution procedures with Apple. Tools Synopsys released new versions of its HSPICE, FineSim and CustomSim circuit simulation products, adding n... » 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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