In early 2015, Synopsys' DesignWare EV5x processor core family achieved notable attention for its unique co-processor engine focused on CNNs (convolutional neural networks) for object recognition and other vision functions.

Modern SoCs increasingly contain a variety of processing resources: one or more CPU cores and a GPU, often with a DSP, programmable logic, or one or multiple special-purpose co-processors for tasks such as computer vision. Properly harnessed, such heterogeneous processors often deliver impressive performance at low cost and low power consumption. But mapping applications onto heterogeneous processors is challenging.

The Internet of Things is an interesting phenomenon.  Most people involved in technology seem to believe that the IoT growing fast, and that it represents a big opportunity.  But most people also find it difficult to say exactly what the Internet of Things actually is, and how, ultimately, it will deliver substantial value for consumers, businesses and other organizations.

I believe that the value of the Internet of Things is that it’s a way to tap into vast amounts of data from the physical world, and extract insights from that data.

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 challenging for both small and large companies and can pose a particular challenge when your new product breaks the mold.

Just last October, Cadence announced the then-latest generation in its computer vision processor core roadmap, the Tensilica Vision P5. Only seven months later, the Vision P5 has been superseded by the Vision P6 (Figure 1). This rapid product development pace reflects the equally rapid expansion and evolution of embedded computer vision applications.

In late January of this year, Movidius and Google broadened their collaboration plans, which had begun with 2014's Project Tango prototype depth-sensing smartphone.

Last summer, I wrote that the time was ripe for deployment of neural networks in mass-market applications.

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?

Convolutional neural networks (CNNs) and other "deep learning" techniques are finding increasing use in a variety of detection and recognition tasks:  identifying music clips and speech phrases, for example, and finding human faces and other objects in images and videos. As a result, we’ve been covering deep learning concepts and implementations regularly in InsideDSP columns and news articles.

We've been hearing a lot about autonomous cars lately – and for good reasons. Driverless cars offer enormous opportunities for improved safety, convenience, and efficiency. Their proliferation may have as profound an impact on our society as conventional automobiles have had over the past century. But deploying autonomous cars widely is going to take a while, given the complexity of the application and the associated technological and regulatory challenges.

In the meantime, autonomous vehicles of other kinds are already on sale.