Deep Learning Neural Networks Drive Demands On Memory Bandwidth


A deep neural network (DNN) is a system that is designed similar to our current understanding of biological neural networks in the brain. DNNs are finding use in many applications, advancing at a fast pace, pushing the limits of existing silicon, and impacting the design of new computing architectures. Figure 1 shows a very basic form of neural network that has several nodes in each layer that ... » read more

Where The Rubber Hits The Road: Implementing Machine Learning On Silicon


Machine learning (ML) is everywhere these days. The common thread between advanced driver-assistance systems (ADAS) vision applications in our cars and the voice (and now facial) recognition applications in our phones is that ML algorithms are doing the heavy lifting, or more accurately, the inferencing. In fact, neural networks (NN) can even be used in application spaces such as file compressi... » read more

Finding And Fixing ML’s Flaws


OneSpin CEO Raik Brinkmann sat down with Semiconductor Engineering to discuss how to make machine learning more robust, predictable and consistent, and new ways to identify and fix problems that may crop up as these systems are deployed. What follows are excerpts of that conversation. SE: How do we make sure devices developed with machine learning behave as they're supposed to, and how do we... » read more

Neural Nets In ADAS And Autonomous Driving SoC Designs


Automotive electronics has ushered in a new wave of semiconductor design innovation and one new technology gaining a lot of attention is neural networks (NNs). Advanced driving assistance systems (ADAS) and autonomous car designs now rely on NNs to meet the real-time requirements of complex object-recognition algorithms. The concept of NNs has been around since World War II, promising a futu... » read more

Scaling Up Vision And AI DSP Performance


Imagine these futuristic scenarios: you hold your phone up to your face, and it automatically recognizes you and unlocks, so you can access content. A sensor at your front door recognizes that you are not an intruder, no matter what the wind has done to your hair or whether your face is obscured by a scarf. How about an autonomous car that recognizes your driving style, so not only can you turn... » read more

Packing Neural Networks Into End-User Client Devices


Most of today’s neural networks can only run on high-performance servers. There’s a big push to change this and simplify network processing to the point where the algorithms can run on end-user client devices. One approach is to eliminate complexity by replacing floating-point representation with fixed-point representation. We take a different approach, and recommend a mix of the two, so as... » read more

AI: The Next Big Thing


The next big thing isn't actually a thing. It's a set of finely tuned statistical models. But developing, optimizing and utilizing those models, which collectively fit under the umbrella of artificial intelligence, will require some of the most advanced semiconductors ever developed. The demand for artificial intelligence is almost ubiquitous. As with all "next big things," it is a horizonta... » read more

Power/Performance Bits: Mar. 6


Neural network chip Neural networks are both slow and consume a lot of power. This made researchers at MIT examine the important aspects of the nodes within a neural network and to see how each part of the computation could be improved. The outcome was a dedicated chip that increases the speed of neural-network computations by three to seven times over its predecessors, while reducing power c... » read more

Verification Of Functional Safety


The automotive industry is grappling with a tradeoff between cost and safety. Safety is well understood in industries that are cost-insensitive, such as aerospace and medical, and the consumer industry has a long track record of driving down costs while increasing functionality. But can these two industries be brought together in a safe and effective manner to enable automobiles to achieve the ... » read more

System Bits: Feb. 13


Enabling individual manufacturing apps Researchers at the Fraunhofer Institute for Computer Graphics Research IGD focused on Industrie 4.0 recognize that manufacturing is turning toward batch sizes of one and individualized production in what is sometimes referred to as ‘highly customized mass production.’ [caption id="attachment_24131609" align="aligncenter" width="300"] The scanning ... » read more

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