5.11. Supported Ops¶

5.11.1. Ops with Limitations¶

BinaryChannelWiseMeanScalingQuantizer: Quantization operator using binary channel wise scaling.
BinaryMeanScalingQuantizer: Quantization operator using binary scaling.
- Input tensor must have float values.
LinearMidTreadHalfQuantizer: Quantization operator with ‘linear mid tread half’.

tf.layers.AveragePooling2D
- Currently, support only 2D.
- Do not support kernel depth = 1.
tf.concat
- Do not support concat of mixed data types (e.g., quantized values and float values).
- All tensor channels must be equal.
- If inputs are quantized, requires Each input channel size = multiple of 32.
tf.layers.Conv2D
- Support only convolution 2D.
- Do not support transpose.
- Requires kernel size = 1x1 or 3x3 or 5x5.
  - Accelerator is not supported kernel size = 5x5 (CPU supported only).
- Requires Input channel size = multiple of 32, otherwise zero padding is used.
- If output is quantized by later operations, Output channel size = multiple of 32, otherwise output channel size is free from limitation (but performance will be worse).
tf.nn.depth_to_space
- Requires depth of input = multiple of block_size^2 * 32.
tf.nn.fused_batch_norm
- scale, offset, mean, variance, and epsilon must be constants or computable from constants.
tf.linalg.matmul
- Do not support scalar.
tf.layers.max_pooling2d
- Currently, support only 2D.
tf.pad
- Supports only channel-wise paddings.
tf.nn.space_to_depth
- For quantized tensor, requires output depth = (multiple of block_size^2 * 32) or (block_size^2 * {8, 16}).
tf.split
- Currently, all of output tensors must have same shape.
- For quantized tensor, requires number of channel of each output tensor = multiple of 32.