Case Studies

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 “…software is changing at head-spinning rates, driven by advances in how computers process giant amounts of data using artificial-intelligence techniques,” said the Wall Street Journal in a recent article, highlighting the challenge faced by smartphone software
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There is now little question that deep learning is an effective means for a wide range of detection and recognition applications. It is increasingly used in computer vision, where it has vastly improved accuracy rates for object recognition. In some cases, the effectiveness of deep learning has
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The world of high tech abounds with cautionary tales of decisions based on inadequate information. With 20/20 hindsight, it's easy to see where wrong decisions were made, but when you're in the thick of it, you often fail to see the forest for the trees. Careful technology decision-makers
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We're hearing more and more about the effectiveness of deep learning for a growing range of applications. With the surge in the volume of data available for training, and reduction in the cost of computing, technologists have turned to deep neural networks for solutions to compute-intensive
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You know you’ve got a compelling story for your new product: it’s got unique features, addresses the requirements of multiple applications, and is supported by a full ecosystem. Now, how do you convince customers of its benefits and value? Earning customer confidence and trust is
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One key challenge for a technology company is identifying markets for its products. Let’s say you have an idea for entering a new market using your existing technology. How do you know if there’s a fit? How do you realistically assess your risk? With more than 25 years of experience
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With more and more products incorporating computer vision functionality, semiconductor vendors are increasingly designing specialized processors for vision applications. For product developers, this means that identifying the best processor (whether a chip for use in a system design, or an IP core
As computer vision is deployed into a variety of new applications, driven by the emergence of powerful, low-cost, and energy-efficient processors, companies need to find ways to squeeze demanding vision processing algorithms into size-, weight-, power, and cost-constrained systems. Fortunately for
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As embedded processors and applications become increasingly complex, good benchmarks are more important than ever. System designers need good benchmarks to judge whether a processor will meet the needs of their applications, and to make accurate comparisons among processors. Processor developers
Algorithms are the essence of digital signal processing; they are the mathematical "recipes" that transform signals in useful ways. Companies developing new algorithms, or considering purchasing or licensing algorithms, often need to assess whether an algorithm will fit within their