Improving AI Productivity With AI


AI is showing up or proposed for nearly all aspects of chip design, but it also can be used to improve the performance of AI chips and to make engineers more productive earlier in the design process. Matt Graham, product management group director at Cadence, talks with Semiconductor Engineering about the role of AI in identifying patterns that are too complex for the human brain to grasp, how t... » read more

Energy Usage in Layers Of Computing (SLAC)


A technical paper titled “Energy Estimates Across Layers of Computing: From Devices to Large-Scale Applications in Machine Learning for Natural Language Processing, Scientific Computing, and Cryptocurrency Mining” was published by researchers at SLAC National Laboratory and Stanford University. Abstract: "Estimates of energy usage in layers of computing from devices to algorithms have bee... » read more

Welcome To EDA 4.0 And The AI-Driven Revolution


By Dan Yu, Harry Foster, and Tom Fitzpatrick Welcome to the era of EDA 4.0, where we are witnessing a revolutionary transformation in electronic design automation driven by the power of artificial intelligence. The history of EDA can be delineated into distinct periods marked by significant technological advancements that have propelled faster design iterations, improved productivity, and fu... » read more

Renesas / Cyberon Speech Recognition


Traditional voice or speech recognition technology is based on a trained model with specific words or phrases. Natural language processing is word-order independent. This requires large computing power to run the real time data through a neural network. The Cyberon approach is different, allowing the algorithms to run on a small, general purpose MCU. Click here to read more. » read more

New Neural Processors Address Emerging Neural Networks


It’s been ten years since AlexNet, a deep learning convolutional neural network (CNN) model running on GPUs, displaced more traditional vision processing algorithms to win the ImageNet Large Scale Visual Recognition Competition (ILSVRC). AlexNet, and its successors, provided significant improvements in object classification accuracy at the cost of intense computational complexity and large da... » read more

There’s More To Machine Learning Than CNNs


Neural networks – and convolutional neural networks (CNNs) in particular – have received an abundance of attention over the last few years, but they're not the only useful machine-learning structures. There are numerous other ways for machines to learn how to solve problems, and there is room for alternative machine-learning structures. “Neural networks can do all this really comple... » read more

How Will Future Cars Interact With Humans?


Future automobiles may come with a set of controls very different from what we’re used to now. Mechanical knobs and switches already are being replaced by touchscreens, but that's just the beginning. There are a multitude of other possible ways in which drivers can interact with their vehicles, and the list is growing as technology drives down the cost of this new human-machine interface (... » read more

Week in Review: IoT, Security, Auto


Internet of Things The drone episode last month at Gatwick Airport in the United Kingdom forced the cancellation or diversion of more than 1,000 flights over three days. While local police arrested a couple suspected of being behind the drone flights, they were quickly exonerated and released. Questions remain on how airports should respond to such episodes, which are bound to happen again and... » read more

System Bits: May 29


Ultra-low-power sensors carrying genetically engineered bacteria to detect gastric bleeding In order to diagnose bleeding in the stomach or other gastrointestinal problems, MIT researchers have built an ingestible sensor equipped with genetically engineered bacteria. [caption id="attachment_24134598" align="alignleft" width="300"] MIT engineers have designed an ingestible sensor equipped with... » read more

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

← Older posts