Blog Review: June 26


Arm's Krish Nathella and Dam Sunwoo dig into research to make a practical implementation of a temporal data prefetcher that overcomes the huge on- and off-chip storage and traffic overheads usually associated with them. Cadence's Paul McLellan notes that while concerns about uncover bias in computer vision algorithms usually focus on people, a team at Facebook found that object recognition t... » read more

SLAM And DSP Implementation


With the introduction of simultaneous localization and mapping technology, or SLAM, there comes a need for more sophisticated DSPs to handle the required computations. To address this need, Cadence has introduced the Tensilica Vision Q7 DSP to handle the requirements of SLAM, including high performance, low power, and with an ease of development that engineers can leverage to design new and exc... » read more

Blog Review: April 10


Arm's Paul Whatmough discusses the growing use of real-time computer vision on mobile devices and proposes transfer learning as a way to enable neural network workloads on resource-constrained hardware. Cadence's Anton Klotz highlights a collaboration with Imec and TU Eindhoven on cell-aware test that reduces defect simulation time by filtering out defects with equivalent fault effects. M... » read more

Power/Performance Bits: April 8


Predicting battery life Researchers at Stanford University, MIT, and Toyota Research Institute developed a machine learning model that can predict how long a lithium-ion battery can be expected to perform. The researchers' model was trained on a few hundred million data points of batteries charging and discharging. The dataset consists of 124 commercial lithium iron phosphate/graphite cells... » read more

Designing An AI SoC


Susheel Tadikonda, vice president of networking and storage at Synopsys, looks at how to achieve economies of scale in AI chips and where the common elements are across all the different architectures. https://youtu.be/fm0kxnj3DuM » read more

The Winograd Transformation


Cheng Wang, senior vice president of engineering at Flex Logix, explains how the Winograd Transformation applies to convolutional neural networks. https://youtu.be/E7QJUby9x-I » read more

Computer Vision Sees a Bright Future


Computer vision is powering advances in automotive, medical, consumer, and agriculture markets. Because the world of computer vision coupled with machine learning evolves so quickly, teams need a way to design and verify an algorithm while the specifications and requirements evolve without starting over every time there is a change. The only way to successfully develop these systems is to use h... » 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: Dec. 26


Adding learning to computer vision UCLA’s Samueli School of Engineering and Stanford University are working on advanced computer vision technology, using artificial intelligence to help vision systems learn to identify faces, objects and other things on their own, without training by humans. The research team breaks up images into chunks they call “viewlets,” then they have the computer ... » read more

Processors Are Exciting Again


Today is a very exciting time in the world of processor architectures. Domain-specific processor architectures are now fully realized as the best answers to the challenges of low power and high performance for many applications. Advancements in artificial intelligence are leading the way to exciting new experiences and products today and in our future. There have been more advances in deep lear... » read more

← Older posts