------ 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, android.hardware.neuralnetworks@1.1 and android.hardware.neuralnetworks@1.2 HAL interfaces. 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 HAL 1.0, 1.1 and 1.2 operations are currently supported: AndroidNN operator Tensor type supported ADD (FLOAT32,QUANT8_ASYMM) AVERAGE_POOL_2D (FLOAT32,QUANT8_ASYMM) BATCH_TO_SPACE_ND (FLOAT32,QUANT8_ASYMM) CONCATENATION (FLOAT32,QUANT8_ASYMM) CONV_2D (FLOAT32,QUANT8_ASYMM) DEPTHWISE_CONV_2D (FLOAT32,QUANT8_ASYMM) DIV (FLOAT32,QUANT8_ASYMM) DEQUANTIZE (FLOAT32,QUANT8_ASYMM) FLOOR (FLOAT32) FULLY_CONNECTED (FLOAT32,QUANT8_ASYMM) L2_NORMALIZATION (FLOAT32) L2_POOL_2D (FLOAT32,QUANT8_ASYMM) LOCAL_RESPONSE_NORMALIZATION (FLOAT32) LOGISTIC (FLOAT32,QUANT8_ASYMM) LSTM (FLOAT32) MAXIMUM (FLOAT32,QUANT8_ASYMM) MAX_POOL_2D (FLOAT32,QUANT8_ASYMM) MEAN (FLOAT32,QUANT8_ASYMM) MINIMUM (FLOAT32,QUANT8_ASYMM) MUL (FLOAT32,QUANT8_ASYMM) PAD (FLOAT32,QUANT8_ASYMM) PAD_V2 (FLOAT32,QUANT8_ASYMM) PRELU (FLOAT32,QUANT8_ASYMM) QUANTIZE (FLOAT32,QUANT8_ASYMM) QUANTIZED_16BIT_LSTM (QUANT8_ASYMM) RELU (FLOAT32,QUANT8_ASYMM) RELU1 (FLOAT32,QUANT8_ASYMM) RELU6 (FLOAT32,QUANT8_ASYMM) RESHAPE (FLOAT32,QUANT8_ASYMM) RESIZE_BILINEAR (FLOAT32,QUANT8_ASYMM) RESIZE_NEAREST_NEIGHBOR (FLOAT32,QUANT8_ASYMM) SOFTMAX (FLOAT32,QUANT8_ASYMM) SPACE_TO_BATCH_ND (FLOAT32,QUANT8_ASYMM) SPACE_TO_DEPTH_ND (FLOAT32,QUANT8_ASYMM) SQUEEZE (FLOAT32,QUANT8_ASYMM) STRIDED_SLICE (FLOAT32,QUANT8_ASYMM) SUB (FLOAT32,QUANT8_ASYMM) TANH (FLOAT32,QUANT8_ASYMM) TRANSPOSE (FLOAT32,QUANT8_ASYMM) TRANSPOSE_CONV_2D (FLOAT32,QUANT8_ASYMM) 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.