> TensorFlow quantization-aware training supports both asymmetric/symmetric. > We are seeing asymmetric models because it is the default. If we'd like to > start from symmetric approach, set the > [symmetric](https://github.com/tensorflow/tensorflow/blob/r1.13/tensorflow/contrib/quantize/python/quantize_graph.py#L149) > and go on. Which, requires extra effort I think...
You might also consider symmetric signed int8 for weights, and unsigned uint8 for for source and destination, since uint8 will give an extra bit of precision following activations. Intel appears to preferentially support this form in their examples, and their new DLBoost avx512 vector instructions also appear to preferentially support this form. ` https://intel.github.io/mkl-dnn/ex_int8_simplenet.html ` ` https://www.intel.ai/nervana/wp-content/uploads/sites/53/2018/05/Lower-Numerical-Precision-Deep-Learning-Inference-Training.pdf ` ` These instructions enable lower precision multiplies with higher precision accumulates. Multiplying two 8-bit values and accumulating the result to 32-bits requires 3 instructions and requires one of the 8-bit vectors to be in 𝑢𝑛𝑠𝑖𝑔𝑛𝑒𝑑𝑖𝑛𝑡8(𝑢8) format, the other in 𝑠𝑖𝑔𝑛𝑒𝑑𝑖𝑛𝑡8(𝑠8) format with the accumulation in 𝑠𝑖𝑔𝑛𝑒𝑑𝑖𝑛𝑡32(𝑠32) format. ` -- You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub: https://github.com/dmlc/tvm/issues/2351#issuecomment-497968508
