depthwise conv2d, ) 2. TVM can not only deploy our network, but also get a good performance gain by autotuning 3. TVM can support many kinds of hardware platform: Intel/arm CPU, Nividia/arm GPU, VTA…5 ��������������

and those converted from Tensorflow. 5 ， Improve scheduling of batch matrix multiplies. 时”Early autotuning templates improve performance by ~20% e What we're working on: This prevents most compute layers

on GitHub and NGC. Known Issues ‣ In rare cases, cuDNN autotuning could cause a long startup time or a hang. In these cases, disbale autotuning using `torch.backends.cudnn.benchmark = False`. ‣ GNMTv2