OnQ Blog

Artificial intelligence tech in Snapdragon 835: personalized experiences created by machine learning

Apr 13, 2017

Qualcomm products mentioned within this post are offered by Qualcomm Technologies, Inc. and/or its subsidiaries.

As our mobile devices have matured, gaining the ability to connect to the Web, we’ve labeled them as “smart.” But why settle for just smart? Harnessing the power of the Qualcomm Snapdragon 835 processor, developers, and OEMs are taking our devices to the next level, creating new experiences with the aid of machine learning. From superior video and security to your own personal assistant, your Snapdragon device has the ability to operate intelligently — outside of the cloud or Web connection — allowing you to experience your smarter phone in an entirely new way.

Application developers and device manufacturers understand what their users want. They can create a feature or an application that uses machine learning (more specifically, deep neural networks) to improve the performance of a particular task, such as detecting or recognizing objects, filtering out background noise, or recognizing voices or languages. These applications are usually run in the cloud, and depending on the device they’re in, this could be sub-optimal.

The Snapdragon Neural Processing Engine SDK was created to help developers determine where to run their neural network-powered applications on the processor. For example, an audio/speech detection application might run on the Qualcomm Hexagon DSP and an object detection or style transfer application on the Qualcomm Adreno GPU. With the help of the SDK, developers have the flexibility to target the core of choice that best matches the power and performance profile of the intended user experience. The SDK supports convolutional neural networks, LSTMs (Long Short-Term Memory) expressed in Caffe and TensorFlow, as well as conversion tools designed to ensure optimal performance on Snapdragon heterogenous cores.

The Hexagon DSP and its wide vector extensions (HVX) offer an impressive power and performance mix for running neural networks on device. Performance is up to 8X faster and 25X more power efficient than using the CPU, which translates to lower battery consumption overall. In addition to support via the Snapdragon Neural Processing Engine, TensorFlow is directly supported on the Hexagon DSP, giving developers multiple options to run their chosen neural network power apps.

Here are a few applications that could be facilitated by Snapdragon 835 on-device machine learning tech:

Photography: Machine learning can aid in scene classification, real-time noise reduction, and object tracking, making it easier to take the perfect shot, or capture video regardless of the conditions.

VR/AR: With machine learning on your device, VR/AR feature can operate faster and with less lag, so everything from gestures and facial recognition to object tracking and depth perception are an immersive experience.

Voice detection: Your phone’s on-device AI can listen for commands and keywords to help you navigate the data and apps on your device more efficiently, and save power doing so.

Security: With facial recognition software and iris scanning, all operating independently from the cloud, your device can learn to identify, and help protect, you.

Connections: Your Snapdragon device has the ability to filter out distracting background noise during calls for clearer conversations with friends and family.

Qualcomm Technologies’ unique machine learning platform is engineered so devices powered by the Snapdragon 835 can run trained neural networks on your devices without relying on a connection to the cloud. Pretty innovative, right?

Take a look at our previous deep dives into each of the Snapdragon 835 key components — batteryimmersive AR and VRphotos and videoconnectivity, and security — all of which combine to make the Snapdragon 835 mobile platform truly groundbreaking.

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Qualcomm Snapdragon, Qualcomm Hexagon, Qualcomm Adreno, and Qualcomm Haven are products of Qualcomm Technologies, Inc.