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The two main phases of AI development — researching your models and deploying them to production — usually require separate frameworks. You might perform your research and make your improvements using Python-based PyTorch and lots of computational power, then use Caffe2 to deploy a lightweight model to production that will run on lower-power devices like smartphones. Each iteration between the frameworks involves steps, tools and time.
As covered in our Making Sense Out of Sensors in IoT Development blog, sensors provide key data from real world phenomena (like temperature, motion, pressure, etc.). There are many interesting examples of how sensors are being creatively used in different vertical industries.
Co-written by Hongqiang Wang, Raga Ramachandra, and Alex Bourd
Have you started programming on the Qualcomm® Adreno™ GPU yet? For compute-intensive operations, you’ll find cycles in the GPU that you can’t afford to leave on the table.
Your apps can get higher performance with lower power consumption when you optimize your code for specific GPUs. With heterogeneous computing you can offload tasks like image/video processing and machine learning inference from CPU to GPU.
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