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What do you do when your always-on application needs image processing, computer vision or machine learning, but it keeps maxing out the CPU and causing cores to shut down from thermal throttling?
“Must be time to look at parallel processing and heterogeneous computing,” you say, “and maybe offload some of that work from CPU to GPU and DSP.”
Good idea. Why limit your compute-intensive tasks when you can run them on the GPU, DSP and other devices and manage them from host code running on the CPU?
Are you thinking of getting a pet? How about a robotic pet dog?
The idea of a robotic canine companion is awesome, but imagine what one could do for someone with visual impairment. There’s been a focus on health and well-being at Qualcomm® HackMobile this year. We saw some great projects from our interns at Qualcomm HackMobile 2016, and Team Chasers backed this up big time with their Seeing Eye Robot project, which brought in an element of health and well-being.
Are you sitting comfortably, and with good posture? I hope so, because blog number three on Qualcomm® HackMobile is about to begin. We’ve been speaking to the talented participants of our intern hackathon, and getting the inside story on the projects they’ve created.
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.
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