It’s Eternal Spring For AI


The field of Artificial Intelligence (AI) has had many ups and downs largely due to unrealistic expectations created by everyone involved including researchers, sponsors, developers, and even consumers. The “reemergence” of AI has lot to do with recent developments in supporting technologies and fields such as sensors, computing at macro and micro scales, communication networks and progre... » read more

Week in Review – IoT, Security, Autos


Products/Services Cadence Design Systems is working with Adesto Technologies to grow the Expanded Serial Peripheral Interface (xSPI) communication protocol ecosystem, for use in Internet of Things devices. The Cadence Memory Model for xSPI allows customers to ensure optimal use of the octal NOR flash with the host processor in an xSPI system, including support for Adesto’s EcoXiP octal xSPI ... » read more

New Vision Technologies For Real-World Applications


Computer vision – the ability of a machine to ‘infer’ or extract useful information from a two-dimensional image or an uncompressed video stream of images – has the ability to change our lives. It can enable self-driving cars, empower robots or drones to see their way to delivering packages to your doorstep, and can turn your face into a payment method (Figure 1). To achieve these advan... » read more

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

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