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This is the second and final part of a guest post by Vladislav Shimanskiy, one of our Adreno™ engineers. His previous post explained the concepts behind an optimized implementation of device-side matrix multiply (MM) kernels and host-side reference code for Adreno 4xx and 5xx GPU families. In this post, he walks you through OpenCL listings you can use to implement the kernels and host code.
The matrix multiply (MM) operation has become very popular on GPUs thanks to recent interest in deep learning, which depends on convolutions. We’ve been hearing from developers who want to accelerate deep learning (DL) applications on Snapdragon® processors with Adreno™ GPUs.
Meet Manoharan Ramachandran, our developer of the month for October. Manoharan is the creator of Drowsy Driver, a road safety app to keep drivers from falling asleep at the wheel. We talked to him about how Snapdragon® processors were a game-changer in the development of his app, and he also shared with us some of his innovative problem solving techniques!
At Qualcomm®, we are pretty lucky to work with some very talented interns. It also happens that we have a lot of cool tech for them to play with! So, it only seems natural that we should put both interns and tech through their paces with a hackathon! Thus, HackMobile 2016, a hackathon for Qualcomm interns was created.
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