Notes:
- A new kernel fusion API is now available for inference for convolution, bias,
batch normalization, and activations. - This release includes new features and bug fixes
- Group and Depthwise convolutions are now available
- 3D Batch Normalization has been implemented for fully packed tensors
- Dilation for convolutions have been implemented
Changes:
- Fixed bugs in direct convolutions
- Fixed issue with paths when $HOME variable is not set
- Fixed padding issues with 1x1 convolutions
- Added incremental support for fp16
- Added fused kernels for Winograd and direct with bias and activations
- Added a getting started guide for kernel fusion.
- Added group and depthwise API for convolutions
- Added 3-D batch normalization support with 5-D tensors
- Improved max pooling performance
- Improved debug and error reporting information
- Improved documentation for convolutions
Known Issues:
- RNNs do not support fp16
- Training with CNNs does not support fp16