Thanks that sounds like it should be relatively straightforward to integrate.
Ramana
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For per-channel weight quantization, it is fully supported. I don't know much
about TFLite frontend, but our pytorch frontend fully supports per channel
quantization.
This tutorial demonstrate importing per-channel quantized pytorch model.
https://docs.tvm.ai/tutorials/frontend/deploy_prequa
Hello there,
Welcome to the community ! AFAIK, there is nothing in place for signed int8
symmetric quantization support in the tflite frontend yet even in master :
however I believe the underlying codegeneration framework can support it with
the qnn dialect of relay based on this
https://di
Hello,
I am working at ST Microelectronics and am evaluating the TVM technology in our
environment.
I have noticed that in TVM 0.6 version the per-axis .tflite quantized models,
such as generated by TensorflowLite, are not supported. Is anybody working on
adding the support for such models. Ho
