Early examples of machine learning and artificial intelligence surround us today, yet we don’t think of it as such. It’s how our voice messages get transcribed. It’s the algorithms that surface the right information for internet searches. It’s the technology that fuels how Amazon and Netflix make recommendations for us. Machine learning and AI are the foundations for voice and digital assistants, such as Amazon’s Alexa, Microsoft’s Cortana, Apple’s Siri and Google’s assistant.
Vison-related machine learning is also prevalent in our day to day existence through features such facial recognition for tagging on Facebook and discovering funny cat videos on YouTube. While voice assistants garnered all the media attention recently, vision-related machine learning solutions have rapidly evolved. Intel’s AI group’s latest announcements highlight an example of this fast-moving progress.
Intel’s Movidius team, part of the new Artificial Intelligence Products Group, announced the third generation of its Vision Processing Unit (VPU) platform called the Myriad X family in late August. The Myriad X platform is a computer vision system on a chip (SOC). The architecture includes a neural network compute engine, which provides the onboard power necessary to support machine learning deep learning and artificial intelligence applications. The architecture offers imaging and vision accelerators to be used for items such as stereo depth, feature extraction and warp/dewarp.
The result of changes in the platform is an overall 10x improvement in the available floating-point performance on DNNs compared to the previous generation. These means faster, more granular and precise image recognition for applications where it matters such as autonomous driving.
This announcement follows quickly on the heels of its July launch of the Movidius Neural Compute Stick, a USB-based development kit for ultra-low power embedded deep neural networks. It a deep learning accelerator and development kit that enables deep learning inference and artificial intelligence (AI) applications at the edge. It can be used to add vision processing capability to low-end computing environments, such as Raspberry Pi. It’s nearly one year since Intel’s acquisition of Movidius, and it’s good to see the company launch two new products within this timeframe.
What does this mean for a company’s digital transformation efforts?
Faster, more accurate vision processing can improve multiple business cases. Remi El-Ouazzane, Intel’s Vice President of the New Technologies Group & General Manager of Movidius, said he believes vision can improve every industry but outlined four clear near-term use cases where VPUs can be deployed today that include drones, augmented reality, security and surveillance as well as robotics.
Each of these applications employs multiple Convolutional Neural Network (CNNs) in parallel. CNNs mimic the biologic structure of human sight and use relatively little pre-processing compared to other image classification algorithms. The CNN network learns the filters that in traditional algorithms that previously had to be designed by data scientists.
The marketplace needed a new type of vision processing because real contextual services require companies to aggregate, analyze and act on new sensor information at the edge. For this to work, you need a low powered hardware, cameras, enhanced algorithms and cloud services. Technologies like Myriad X and Intel’s Nervana allow business to embrace vision as a contextual element.
What does it mean for Intel?
PCs and mainframes rule computing but as the IoT market unfolds the number and type of connected computing devices have proliferated. It’s not enough to just provide a connected device in this new world of Internet of Things (IoT) and AI. Devices need to be smart as well. Part of being intelligent requires the ability to process a certain amount of data at the edge where it’s created and integrate that data into business workflows. It makes sense that Intel would focus on this technology because VPUs, deep learning and AI are the foundations for the next generation of connected computing.
However, it’s still early days in this market, and Intel’s not alone. AMD and Nvidia have their take on the machine learning, deep learning and contextual computing landscape. This competition will keep innovation flowing in the space.
This article was originally posted on Forbes.com.
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