root_inp = onnx.helper.make_tensor_value_info("root", onnx.TensorProto.FLOAT, shape) output = onnx.helper.make_tensor_value_info("output", onnx.TensorProto.FLOAT ...
Model quantization converts the high-precision floating-point weights in a neural network (32-bit or 16-bit) into compact lower-precision representations (8-bit, 4-bit, or even 2-bit integers). This ...
Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...
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