Deep Learning Spreads

Deep learning is gaining traction across a broad swath of applications, providing more nuanced and complex behavior than machine learning offers today. Those attributes are particularly important for safety-critical devices, such as assisted or autonomous vehicles, as well as for natural language processing where a machine can recognize the intent of words based upon the context of a convers... » read more

System Bits: Jan. 2

Robots imagine their future to learn By playing with objects and then imagining how to get the task done, UC Berkeley researchers have developed a robotic learning technology that enables robots to figure out how to manipulate objects they have never encountered before. The team expects this technology could help self-driving cars anticipate future events on the road and produce more intel... » 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

Applying Machine Learning

Sundari Mitra, co-founder and CEO of NetSpeed Systems, sat down with Semiconductor Engineering to discuss machine learning, training algorithms, what customers are struggling with today, and how startups fare in an increasingly consolidated semiconductor industry. What follows are excerpts of that conversation. SE: Machine learning is booming. How will this change design? Mitra: This is a... » 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

Speeding Up Neural Networks

Neural networking is gaining traction as the best way of collecting and moving critical data from the physical world and processing it in the digital world. Now the question is how to speed up this whole process. But it isn't a straightforward engineering challenge. Neural networking itself is in a state of almost constant flux and development, which makes it something of a moving target. Th... » read more

The Efficiency Problem

The field of automotive automation has been the driver – so to speak – of the next leap of innovation in the field of transportation. Car architectures are being re-engineered to take advantage of incredible leaps in automation, using more powerful processors that process more data than ever before. The recent focus on autonomous automobile technology could be due to the ongoing drop in ... » read more

Supporting CPUs Plus FPGAs

While it has been possible to pair a CPU and FPGA for quite some time, two things have changed recently. First, the industry has reduced the latency of the connection between them and second, we now appear to have the killer app for this combination. Semiconductor Engineering sat down to discuss these changes and the state of the tool chain to support this combination, with Kent Orthner, system... » read more

What Does An AI Chip Look Like?

Depending upon your point of reference, artificial intelligence will be the next big thing or it will play a major role in all of the next big things. This explains the frenzy of activity in this sector over the past 18 months. Big companies are paying billions of dollars to acquire startup companies, and even more for R&D. In addition, governments around the globe are pouring additional... » read more

System Bits: June 28

Deep-learning-based virtual reality tool Given that future systems which enable people to interact with virtual environments will require computers to interpret the human hand’s nearly endless variety and complexity of changing motions and joint angles, Purdue University researchers have created a convolutional neural network-based system that is capable of deep learning. [caption id="att... » read more

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