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by Marat_Dukhan
2783 days ago
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QNNPACK directly competes with the CPU backend of TensorFlow Lite and the gemmlowp library. The Caffe2 backend of PyTorch 1.0 integrates QNNPACK, and directly competes with TensorFlow Lite. QNNPACK targets only mobile CPUs, but Caffe2 integrates other backends for non-CPU targets, e.g. Apple's MPSCNN library for iPhone GPUs, Qualcomm's Snapdragon NPE for Qualcomm GPUs and DSPs, ARM ComputeLibrary for Android GPUs. Not sure what you mean by TensorFlow Cores: NVIDIA has TensorCores and TensorRT, and Google has Tensor Processing Units (TPU), but neither of these technologies are for mobile. |
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I was referring to TensorRT from Nvidia and TPUs from Google.
One of the strength of the TFLite API is that the same exported tflite model can run on both mobiles and servers. It may make less sense to run lite models on servers, because of the loss of precision but it may also have its own use case for very big models on cheap servers.
Nvidia sells Android devices and embedded boards for robotic, which will surely have some sort of TensorRT-derived cores if not already. Goole could one day integrate their specialized cores (security and TPUs) into their phones too, or into AI-oriented IoT devices.