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-# Caffe layers supported by the Arm NN SDK
-This reference guide provides a list of Caffe layers the Arm NN SDK currently supports.
-
-Although some other neural networks might work, Arm tests the Arm NN SDK with Caffe implementations of the following neural networks: 
-
-- AlexNet.
-- Cifar10.
-- Inception-BN.
-- Resnet_50, Resnet_101 and Resnet_152.
-- VGG_CNN_S, VGG_16 and VGG_19.
-- Yolov1_tiny.
-- Lenet.
-- MobileNetv1.
-- SqueezeNet v1.0 and SqueezeNet v1.1
-
-The Arm NN SDK supports the following machine learning layers for Caffe networks:
-
-
-- Argmax, excluding the top_k and out_max_val parameters.
-- BatchNorm, in inference mode.
-- Convolution, excluding Weight Filler, Bias Filler, Engine, Force nd_im2col, and Axis parameters.
-- Deconvolution, excluding the Dilation Size, Weight Filler, Bias Filler, Engine, Force nd_im2col, and Axis parameters.
-
-  Caffe doesn't support depthwise convolution, the equivalent layer is implemented through the notion of groups. ArmNN supports groups this way:
-  - when group=1, it is a normal conv2d
-  - when group=#input_channels, we can replace it by a depthwise convolution
-  - when group>1 && group<#input_channels, we need to split the input into the given number of groups, apply a separate convolution and then merge the results
-- Concat, along the channel dimension only.
-- Dropout, in inference mode.
-- Eltwise, excluding the coeff parameter.
-- Inner Product, excluding the Weight Filler, Bias Filler, Engine, and Axis parameters.
-- Input.
-- LRN, excluding the Engine parameter.
-- Pooling, excluding the Stochastic Pooling and Engine parameters.
-- ReLU.
-- Scale.
-- Softmax, excluding the Axis and Engine parameters.
-- Split.
-
-More machine learning layers will be supported in future releases.