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authorsurmeh01 <surabhi.mehta@arm.com>2018-05-17 14:11:25 +0100
committertelsoa01 <telmo.soares@arm.com>2018-05-23 16:23:49 +0100
commit49b9e100bfbb3b8da01472a0ff48b2bd92944e01 (patch)
tree1a998fa12f665ff0a15b299d8bae5590e0aed884 /NnapiSupport.txt
parent28adb40e1bb1d3f3a06a7f333f7f2a4f42d3ed4b (diff)
downloadandroid-nn-driver-49b9e100bfbb3b8da01472a0ff48b2bd92944e01.tar.gz
Release 18.05
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+------ ArmNN for Android NNAPI supported operations ------
+
+This release of ArmNN for Android supports use as a driver for the Android Neural Networks API. It implements the android.hardware.neuralnetworks@1.0 interface.
+
+For more information on the Android Neural Networks API, see https://developer.android.com/ndk/guides/neuralnetworks/index.html
+
+For integration and usage documentation, please see README.md.
+
+--- Support for Android Neural Networks HAL operations ---
+
+The following AndroidNN operations are currently supported.
+
+AndroidNN operator Tensor type supported
+ADD (FLOAT32)
+AVERAGE_POOL_2D (FLOAT32,QUANT8_ASYMM)
+CONCATENATION (FLOAT32)
+CONV_2D (FLOAT32,QUANT8_ASYMM)
+DEPTHWISE_CONV_2D* (FLOAT32,QUANT8_ASYMM)
+FLOOR (FLOAT32)
+FULLY_CONNECTED (FLOAT32)
+L2_NORMALIZATION (FLOAT32)
+L2_POOL_2D (FLOAT32)
+LOCAL_RESPONSE_NORMALIZATION (FLOAT32)
+LOGISTIC (FLOAT32,QUANT8_ASYMM)
+MAX_POOL_2D (FLOAT32,QUANT8_ASYMM)
+MUL (FLOAT32)
+RELU (FLOAT32,QUANT8_ASYMM)
+RELU1 (FLOAT32,QUANT8_ASYMM)
+RELU6 (FLOAT32,QUANT8_ASYMM)
+RESHAPE (FLOAT32,QUANT8_ASYMM)
+RESIZE_BILINEAR (FLOAT32)
+SOFTMAX (FLOAT32,QUANT8_ASYMM)
+TANH (FLOAT32)
+
+* Depthwise convolution only supports a value of 1 for the depth multiplier. In addition, the QUANT8_ASYMM version only supports 3x3 kernels.
+
+--- Unsupported operators ---
+
+The following AndroidNN operations are currently not supported.
+
+DEPTH_TO_SPACE
+DEQUANTIZE
+EMBEDDING_LOOKUP
+HASHTABLE_LOOKUP
+LSH_PROJECTION
+LSTM
+RNN
+SPACE_TO_DEPTH
+SVDF
+
+Where operations are not supported by the ArmNN Android NN Driver, the driver indicates this to the framework appropriately and the framework implements those operations using a CPU implementation.