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Co-written with Hongqiang Wang and Alex Bourd
Continuing our series on OpenCL optimization on the Qualcomm® Adreno™ GPU, we describe a multi-step optimization for apps that use the Epsilon filter. We demonstrate that OpenCL optimizations of the Epsilon filter on the GPU are device-specific.
Qualcomm Developer Network November Developer of the Month is Henry Ruiz. Henry is from Cali, Colombia where he completed his Computer Systems Engineering degree from Antonio Jose Camacho University. In 2017 he completed a Master’s degree in Artificial Intelligence from the Autonomous University of Baja California (UABC). He has experience in Machine Learning, Deep Learning, Computer Vision and Artificial Intelligence.
Are you ready to create IoT solutions like home monitoring cameras, enterprise security cameras and smart home devices with built-in artificial intelligence (AI)? The hardware, software and cloud components are now in place for you to get started.
In our previous blog, Tips to Power Through Sensor Development Challenges, we saw how sensors provide approximate measurements of real-world phenomena. These different types of sensors can exhibit inaccuracies from various things such as its design, environmental conditions, and others. We also touched upon solutions for overcoming these challenges such as sensor fusion–the practice of gathering and processing data from multiple sensors.
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