Posted by Alan Kelly, Software Engineer
for XNNPACK is now available in TensorFlow 2.9.1 and later.If you are using TensorFlow Lite 2.9.1 or later, it gives the per operator profile even for the section that is delegated to XNNPACK so that you no longer need to decide between fast inference and detailed performance information. The operator name, data layout (NHWC for example), datatype (FP32) and microkernel type (if applicable) are shown.
| The most expensive operators are listed. In this example, you can see that a deconvolution accounted for 33.91% of the total runtime. |
XNNPACK can also perform inference in half-precision (16 bit) floating point format if the hardware supports these operations natively, and IEEE16 inference is supported for every floating-point operator in the model, and the model’s `reduced_precision_support` metadata indicates that it is compatible with FP16 inference. FP16 inference can also be forced. More information is available
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