MLECO-2599: Replace DSCNN with MicroNet for KWS

Added SoftMax function to Mathutils to allow MicroNet
to output probability as it does not nativelu have this layer.
Minor refactoring to accommodate Softmax Calculations
Extensive renaming and updating of documentation and resource download script.
Added SoftMax function to Mathutils to allow MicroNet
to output probability.

Change-Id: I7cbbda1024d14b85c9ac1beea7ca8fbffd0b6eb5
Signed-off-by: Liam Barry <liam.barry@arm.com>

4 files changed, 102 insertions, 109 deletions

diff --git a/docs/documentation.md b/docs/documentation.md
index f1fab8c..2ec2028 100644
--- a/docs/documentation.md
+++ b/docs/documentation.md
@@ -211,7 +211,7 @@ What these folders contain:
 The models used in the use-cases implemented in this project can be downloaded from: [Arm ML-Zoo](https://github.com/ARM-software/ML-zoo).
 
 - [Mobilenet V2](https://github.com/ARM-software/ML-zoo/tree/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_int8)
-- [DS-CNN](https://github.com/ARM-software/ML-zoo/tree/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b//models/keyword_spotting/ds_cnn_large/tflite_clustered_int8)
+- [MicroNet for Keyword Spotting](https://github.com/ARM-software/ML-zoo/tree/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8)
 - [Wav2Letter](https://github.com/ARM-software/ML-zoo/tree/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8)
 - [MicroNet for Anomaly Detection](https://github.com/ARM-software/ML-zoo/tree/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8)
 - [MicroNet for Visual Wake Word](https://github.com/ARM-software/ML-zoo/raw/7dd3b16bb84007daf88be8648983c07f3eb21140/models/visual_wake_words/micronet_vww4/tflite_int8/vww4_128_128_INT8.tflite)
diff --git a/docs/quick_start.md b/docs/quick_start.md
index 6522bdd..252b084 100644
--- a/docs/quick_start.md
+++ b/docs/quick_start.md
@@ -84,11 +84,11 @@ curl -L https://github.com/ARM-software/ML-zoo/raw/e0aa361b03c738047b9147d1a50e3
     --output ./resources_downloaded/img_class/ifm0.npy
 curl -L https://github.com/ARM-software/ML-zoo/raw/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/testing_output/MobilenetV2/Predictions/Reshape_11/0.npy \
     --output ./resources_downloaded/img_class/ofm0.npy
-curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/ds_cnn_clustered_int8.tflite \
-    --output ./resources_downloaded/kws/ds_cnn_clustered_int8.tflite
-curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_input/input_2/0.npy \
+curl -L https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/kws_micronet_m.tflite \
+    --output ./resources_downloaded/kws/kws_micronet_m.tflite
+curl -L https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_input/input/0.npy \
     --output ./resources_downloaded/kws/ifm0.npy
-curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_output/Identity/0.npy \
+curl -L https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_output/Identity/0.npy \
     --output ./resources_downloaded/kws/ofm0.npy
 curl -L https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/wav2letter_pruned_int8.tflite \
     --output ./resources_downloaded/kws_asr/wav2letter_pruned_int8.tflite
@@ -96,11 +96,11 @@ curl -L https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59d
     --output ./resources_downloaded/kws_asr/asr/ifm0.npy
 curl -L https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/testing_output/Identity_int8/0.npy \
     --output ./resources_downloaded/kws_asr/asr/ofm0.npy
-curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/ds_cnn_clustered_int8.tflite \
-    --output ./resources_downloaded/kws_asr/ds_cnn_clustered_int8.tflite
-curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_input/input_2/0.npy \
+curl -L https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/kws_micronet_m.tflite \
+    --output ./resources_downloaded/kws_asr/kws_micronet_m.tflite
+curl -L https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_input/input/0.npy \
     --output ./resources_downloaded/kws_asr/kws/ifm0.npy
-curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_output/Identity/0.npy \
+curl -L https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_output/Identity/0.npy \
     --output ./resources_downloaded/kws_asr/kws/ofm0.npy
 curl -L https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/rnnoise_INT8.tflite \
     --output ./resources_downloaded/noise_reduction/rnnoise_INT8.tflite
@@ -131,22 +131,22 @@ curl -L https://github.com/ARM-software/ML-zoo/raw/7dd3b16bb84007daf88be8648983c
 curl -L https://github.com/ARM-software/ML-zoo/raw/7dd3b16bb84007daf88be8648983c07f3eb21140/models/visual_wake_words/micronet_vww4/tflite_int8/testing_output/Identity/0.npy \
     --output ./resources_downloaded/vww/ofm0.npy
 
-. resources_downloaded/env/bin/activate && vela resources_downloaded/kws/ds_cnn_clustered_int8.tflite \
+. resources_downloaded/env/bin/activate && vela resources_downloaded/kws/kws_micronet_m.tflite \
     --accelerator-config=ethos-u55-128 \
     --optimise Performance --config scripts/vela/default_vela.ini \
     --memory-mode=Shared_Sram \
     --system-config=Ethos_U55_High_End_Embedded \
     --output-dir=resources_downloaded/kws \
     --arena-cache-size=2097152
-mv resources_downloaded/kws/ds_cnn_clustered_int8_vela.tflite resources_downloaded/kws/ds_cnn_clustered_int8_vela_H128.tflite
+mv resources_downloaded/kws/kws_micronet_m.tflite resources_downloaded/kws/kws_micronet_m_vela_H128.tflite
 
-. resources_downloaded/env/bin/activate && vela resources_downloaded/kws/ds_cnn_clustered_int8.tflite \
+. resources_downloaded/env/bin/activate && vela resources_downloaded/kws/kws_micronet_m.tflite \
     --accelerator-config=ethos-u65-256 \
     --optimise Performance --config scripts/vela/default_vela.ini \
     --memory-mode=Dedicated_Sram \
     --system-config=Ethos_U65_High_End \
     --output-dir=resources_downloaded/kws
-mv resources_downloaded/kws/ds_cnn_clustered_int8_vela.tflite resources_downloaded/kws/ds_cnn_clustered_int8_vela_Y256.tflite
+mv resources_downloaded/kws/kws_micronet_vela.tflite resources_downloaded/kws/kws_micronet_m_vela_Y256.tflite
 
 . resources_downloaded/env/bin/activate && vela resources_downloaded/kws_asr/wav2letter_int8.tflite \
     --accelerator-config=ethos-u55-128 \
@@ -165,22 +165,22 @@ mv resources_downloaded/kws_asr/wav2letter_int8_vela.tflite resources_downloaded
     --output-dir=resources_downloaded/kws_asr
 mv resources_downloaded/kws_asr/wav2letter_int8_vela.tflite resources_downloaded/kws_asr/wav2letter_int8_vela_Y256.tflite
 
-. resources_downloaded/env/bin/activate && vela resources_downloaded/kws_asr/ds_cnn_clustered_int8.tflite \
+. resources_downloaded/env/bin/activate && vela resources_downloaded/kws_asr/kws_micronet_m.tflite \
     --accelerator-config=ethos-u55-128 \
     --optimise Performance --config scripts/vela/default_vela.ini \
     --memory-mode=Shared_Sram \
     --system-config=Ethos_U55_High_End_Embedded \
     --output-dir=resources_downloaded/kws_asr \
     --arena-cache-size=2097152
-mv resources_downloaded/kws_asr/ds_cnn_clustered_int8_vela.tflite resources_downloaded/kws_asr/ds_cnn_clustered_int8_vela_H128.tflite
+mv resources_downloaded/kws_asr/kws_micronet_m.tflite_vela.tflite resources_downloaded/kws_asr/kws_micronet_m.tflite_vela_H128.tflite
 
-. resources_downloaded/env/bin/activate && vela resources_downloaded/kws_asr/ds_cnn_clustered_int8.tflite \
+. resources_downloaded/env/bin/activate && vela resources_downloaded/kws_asr/kws_micronet_m.tflite \
     --accelerator-config=ethos-u65-256 \
     --optimise Performance --config scripts/vela/default_vela.ini \
     --memory-mode=Dedicated_Sram \
     --system-config=Ethos_U65_High_End \
     --output-dir=resources_downloaded/kws_asr
-mv resources_downloaded/kws_asr/ds_cnn_clustered_int8_vela.tflite resources_downloaded/kws_asr/ds_cnn_clustered_int8_vela_Y256.tflite
+mv resources_downloaded/kws_asr/kws_micronet_m.tflite_vela.tflite resources_downloaded/kws_asr/kws_micronet_m.tflite_vela_Y256.tflite
 
 . resources_downloaded/env/bin/activate && vela resources_downloaded/inference_runner/dnn_s_quantized.tflite \
     --accelerator-config=ethos-u55-128 \
diff --git a/docs/use_cases/kws.md b/docs/use_cases/kws.md
index 84eddd6..0c50fe5 100644
--- a/docs/use_cases/kws.md
+++ b/docs/use_cases/kws.md
@@ -23,7 +23,7 @@ Use-case code could be found in the following directory: [source/use_case/kws](.
 
 ### Preprocessing and feature extraction
 
-The `DS-CNN` keyword spotting model that is used with the Code Samples expects audio data to be preprocessed in a
+The `MicroNet` keyword spotting model that is used with the Code Samples expects audio data to be preprocessed in a
 specific way before performing an inference.
 
 Therefore, this section aims to provide an overview of the feature extraction process used.
@@ -62,7 +62,7 @@ used.
 ### Postprocessing
 
 After an inference is complete, the word with the highest detected probability is output to console. Providing that the
-probability is larger than a threshold value. The default is set to `0.9`.
+probability is larger than a threshold value. The default is set to `0.7`.
 
 If multiple inferences are performed for an audio clip, then multiple results are output.
 
@@ -107,7 +107,7 @@ In addition to the already specified build option in the main documentation, the
   this number, then it is padded with zeros. The default value is `16000`.
 
 - `kws_MODEL_SCORE_THRESHOLD`: Threshold value that must be applied to the inference results for a label to be deemed
-  valid. Goes from 0.00 to 1.0. The default is `0.9`.
+  valid. Goes from 0.00 to 1.0. The default is `0.7`.
 
 - `kws_ACTIVATION_BUF_SZ`: The intermediate, or activation, buffer size reserved for the NN model. By default, it is set
   to 2MiB and is enough for most models
@@ -247,7 +247,7 @@ For further information: [Optimize model with Vela compiler](../sections/buildin
 
 To run the application with a custom model, you must provide a `labels_<model_name>.txt` file of labels that are
 associated with the model. Each line of the file must correspond to one of the outputs in your model. Refer to the
-provided `ds_cnn_labels.txt` file for an example.
+provided `micronet_kws_labels.txt` file for an example.
 
 Then, you must set `kws_MODEL_TFLITE_PATH` to the location of the Vela processed model file and `kws_LABELS_TXT_FILE`to
 the location of the associated labels file.
@@ -369,24 +369,24 @@ What the preceding choices do:
     INFO - Model INPUT tensors:
     INFO -  tensor type is INT8
     INFO -  tensor occupies 490 bytes with dimensions
-    INFO -    0:   1
-    INFO -    1:   1
-    INFO -    2:  49
-    INFO -    3:  10
+    INFO - 		0:   1
+    INFO - 		1:  49
+    INFO - 		2:  10
+    INFO - 		3:   1
     INFO - Quant dimension: 0
-    INFO - Scale[0] = 1.107164
-    INFO - ZeroPoint[0] = 95
+    INFO - Scale[0] = 0.201095
+    INFO - ZeroPoint[0] = -5
     INFO - Model OUTPUT tensors:
-    INFO -  tensor type is INT8
-    INFO -  tensor occupies 12 bytes with dimensions
-    INFO -    0:   1
-    INFO -    1:  12
+    INFO - 	tensor type is INT8
+    INFO - 	tensor occupies 12 bytes with dimensions
+    INFO - 		0:   1
+    INFO - 		1:  12
     INFO - Quant dimension: 0
-    INFO - Scale[0] = 0.003906
-    INFO - ZeroPoint[0] = -128
-    INFO - Activation buffer (a.k.a tensor arena) size used: 72848
-    INFO - Number of operators: 1
-    INFO -  Operator 0: ethos-u
+    INFO - Scale[0] = 0.056054
+    INFO - ZeroPoint[0] = -54
+    INFO - Activation buffer (a.k.a tensor arena) size used: 127068
+    INFO - Number of operators: 0
+    INFO - 	Operator 0: ethos-u
     ```
 
 5. List audio clips: Prints a list of pair ... indexes. The original filenames are embedded in the application, like so:
@@ -405,18 +405,21 @@ Please select the first menu option to execute inference on the first file.
 
 The following example illustrates the output for classification:
 
-```logINFO - Running inference on audio clip 0 => down.wav
+```log
+
+INFO - Running inference on audio clip 0 => down.wav
 INFO - Inference 1/1
 INFO - Final results:
 INFO - Total number of inferences: 1
-INFO - For timestamp: 0.000000 (inference #: 0); label: down, score: 0.996094; threshold: 0.900000
+INFO - For timestamp: 0.000000 (inference #: 0); label: down, score: 0.986182; threshold: 0.700000
 INFO - Profile for Inference:
-INFO - NPU AXI0_RD_DATA_BEAT_RECEIVED beats: 217385
-INFO - NPU AXI0_WR_DATA_BEAT_WRITTEN beats: 82607
-INFO - NPU AXI1_RD_DATA_BEAT_RECEIVED beats: 59608
-INFO - NPU ACTIVE cycles: 680611
-INFO - NPU IDLE cycles: 561
-INFO - NPU TOTAL cycles: 681172
+INFO - NPU AXI0_RD_DATA_BEAT_RECEIVED beats: 132130
+INFO - NPU AXI0_WR_DATA_BEAT_WRITTEN beats: 48252
+INFO - NPU AXI1_RD_DATA_BEAT_RECEIVED beats: 17544
+INFO - NPU ACTIVE cycles: 413814
+INFO - NPU IDLE cycles: 358
+INFO - NPU TOTAL cycles: 414172
+
 ```
 
 On most systems running Fast Model, each inference takes under 30 seconds.
@@ -425,22 +428,22 @@ The profiling section of the log shows that for this inference:
 
 - *Ethos-U* PMU report:
 
-  - 681,172 total cycle: The number of NPU cycles.
+  - 414,172 total cycle: The number of NPU cycles.
 
-  - 680,611 active cycles: The number of NPU cycles that were used for computation.
+  - 413,814 active cycles: The number of NPU cycles that were used for computation.
 
-  - 561 idle cycles: The number of cycles for which the NPU was idle.
+  - 358 idle cycles: The number of cycles for which the NPU was idle.
 
-  - 217,385 AXI0 read beats: The number of AXI beats with read transactions from the AXI0 bus. AXI0 is the bus where the
+  - 132,130 AXI0 read beats: The number of AXI beats with read transactions from the AXI0 bus. AXI0 is the bus where the
     *Ethos-U* NPU reads and writes to the computation buffers, activation buf, or tensor arenas.
 
-  - 82,607 write cycles: The number of AXI beats with write transactions to AXI0 bus.
+  - 48,252 write cycles: The number of AXI beats with write transactions to AXI0 bus.
 
-  - 59,608 AXI1 read beats: The number of AXI beats with read transactions from the AXI1 bus. AXI1 is the bus where the
+  - 17,544 AXI1 read beats: The number of AXI beats with read transactions from the AXI1 bus. AXI1 is the bus where the
     *Ethos-U* NPU reads the model. So, read-only.
 
 - For FPGA platforms, a CPU cycle count can also be enabled. However, do not use cycle counters for FVP, as the CPU
   model is not cycle-approximate or cycle-accurate.
 
-> **Note:** The application prints the highest confidence score and the associated label from the `ds_cnn_labels.txt`
+> **Note:** The application prints the highest confidence score and the associated label from the `micronet_kws_labels.txt`
 > file.
diff --git a/docs/use_cases/kws_asr.md b/docs/use_cases/kws_asr.md
index 42c9d3a..22f1e9d 100644
--- a/docs/use_cases/kws_asr.md
+++ b/docs/use_cases/kws_asr.md
@@ -44,7 +44,7 @@ By default, the KWS model is run purely on the CPU and **not** on the *Ethos-U55
 
 #### Keyword Spotting Preprocessing
 
-The `DS-CNN` keyword spotting model that is used with the Code Samples expects audio data to be preprocessed in a
+The `MicroNet` keyword spotting model that is used with the Code Samples expects audio data to be preprocessed in a
 specific way before performing an inference.
 
 Therefore, this section aims to provide an overview of the feature extraction process used.
@@ -455,43 +455,30 @@ What the preceding choices do:
 
 4. Show NN model info: Prints information about the model data type, input, and output, tensor sizes:
 
-    ```log
+   ```log
+    INFO - Model info:
     INFO - Model INPUT tensors:
     INFO -  tensor type is INT8
     INFO -  tensor occupies 490 bytes with dimensions
-    INFO -    0:   1
-    INFO -    1:   1
-    INFO -    2:  49
-    INFO -    3:  10
+    INFO - 		0:   1
+    INFO - 		1:  49
+    INFO - 		2:  10
+    INFO - 		3:   1
     INFO - Quant dimension: 0
-    INFO - Scale[0] = 1.107164
-    INFO - ZeroPoint[0] = 95
+    INFO - Scale[0] = 0.201095
+    INFO - ZeroPoint[0] = -5
     INFO - Model OUTPUT tensors:
-    INFO -  tensor type is INT8
-    INFO -  tensor occupies 12 bytes with dimensions
-    INFO -    0:   1
-    INFO -    1:  12
+    INFO - 	tensor type is INT8
+    INFO - 	tensor occupies 12 bytes with dimensions
+    INFO - 		0:   1
+    INFO - 		1:  12
     INFO - Quant dimension: 0
-    INFO - Scale[0] = 0.003906
-    INFO - ZeroPoint[0] = -128
-    INFO - Activation buffer (a.k.a tensor arena) size used: 123616
-    INFO - Number of operators: 16
-    INFO -  Operator 0: RESHAPE
-    INFO -  Operator 1: CONV_2D
-    INFO -  Operator 2: DEPTHWISE_CONV_2D
-    INFO -  Operator 3: CONV_2D
-    INFO -  Operator 4: DEPTHWISE_CONV_2D
-    INFO -  Operator 5: CONV_2D
-    INFO -  Operator 6: DEPTHWISE_CONV_2D
-    INFO -  Operator 7: CONV_2D
-    INFO -  Operator 8: DEPTHWISE_CONV_2D
-    INFO -  Operator 9: CONV_2D
-    INFO -  Operator 10: DEPTHWISE_CONV_2D
-    INFO -  Operator 11: CONV_2D
-    INFO -  Operator 12: AVERAGE_POOL_2D
-    INFO -  Operator 13: RESHAPE
-    INFO -  Operator 14: FULLY_CONNECTED
-    INFO -  Operator 15: SOFTMAX
+    INFO - Scale[0] = 0.056054
+    INFO - ZeroPoint[0] = -54
+    INFO - Activation buffer (a.k.a tensor arena) size used: 127068
+    INFO - Number of operators: 1
+    INFO -  Operator 0: ethos-u
+   
     INFO - Model INPUT tensors:
     INFO -  tensor type is INT8
     INFO -  tensor occupies 11544 bytes with dimensions
@@ -511,9 +498,9 @@ What the preceding choices do:
     INFO - Quant dimension: 0
     INFO - Scale[0] = 0.003906
     INFO - ZeroPoint[0] = -128
-    INFO - Activation buffer (a.k.a tensor arena) size used: 809808
+    INFO - Activation buffer (a.k.a tensor arena) size used: 4184332
     INFO - Number of operators: 1
-    INFO -  Operator 0: ethos-u
+    INFO - Operator 0: ethos-u
     ```
 
 5. List audio clips: Prints a list of pair ... indexes. The original filenames are embedded in the application, like so:
@@ -534,27 +521,31 @@ INFO - KWS audio data window size 16000
 INFO - Running KWS inference on audio clip 0 => yes_no_go_stop.wav
 INFO - Inference 1/7
 INFO - For timestamp: 0.000000 (inference #: 0); threshold: 0.900000
-INFO -          label @ 0: yes, score: 0.996094
+INFO -          label @ 0: yes, score: 0.997407
 INFO - Profile for Inference:
-INFO - NPU AXI0_RD_DATA_BEAT_RECEIVED beats: 217385
-INFO - NPU AXI0_WR_DATA_BEAT_WRITTEN beats: 82607
-INFO - NPU AXI1_RD_DATA_BEAT_RECEIVED beats: 59608
-INFO - NPU ACTIVE cycles: 680611
-INFO - NPU IDLE cycles: 561
-INFO - NPU TOTAL cycles: 681172
+INFO - NPU AXI0_RD_DATA_BEAT_RECEIVED beats: 132130
+INFO - NPU AXI0_WR_DATA_BEAT_WRITTEN beats: 48252
+INFO - NPU AXI1_RD_DATA_BEAT_RECEIVED beats: 17544
+INFO - NPU ACTIVE cycles: 413814
+INFO - NPU IDLE cycles: 358
+INFO - NPU TOTAL cycles: 414172
 INFO - Keyword spotted
 INFO - Inference 1/2
 INFO - Inference 2/2
-INFO - Result for inf 0: no gow
-INFO - Result for inf 1:  stoppe
-INFO - Final result: no gow stoppe
+INFO - Result for inf 0: no go
+INFO - Result for inf 1:  stop
+INFO - Final result: no go stop
 INFO - Profile for Inference:
-INFO - NPU AXI0_RD_DATA_BEAT_RECEIVED beats: 13520864
-INFO - NPU AXI0_WR_DATA_BEAT_WRITTEN beats: 2841970
-INFO - NPU AXI1_RD_DATA_BEAT_RECEIVED beats: 2717670
-INFO - NPU ACTIVE cycles: 28909309
-INFO - NPU IDLE cycles: 863
-INFO - NPU TOTAL cycles: 28910172
+INFO - NPU AXI0_RD_DATA_BEAT_RECEIVED beats: 8895431
+INFO - NPU AXI0_WR_DATA_BEAT_WRITTEN beats: 1890168
+INFO - NPU AXI1_RD_DATA_BEAT_RECEIVED beats: 1740069
+INFO - NPU ACTIVE cycles: 30164330
+INFO - NPU IDLE cycles: 342
+INFO - NPU TOTAL cycles: 30164672
+INFO - Main loop terminated.
+INFO - program terminating...
+INFO - releasing platform Arm Corstone-300 (SSE-300)
+
 ```
 
 It can take several minutes to complete one inference run. The average time is around 2-3 minutes.
@@ -567,22 +558,21 @@ The profiling section of the log shows that for the ASR inference:
 
 - *Ethos-U* PMU report:
 
-  - 28,910,172 total cycle: The number of NPU cycles.
+  - 30,164,672 total cycle: The number of NPU cycles.
 
-  - 28,909,309 active cycles: The number of NPU cycles that were used for computation.
+  - 30,164,330 active cycles: The number of NPU cycles that were used for computation.
 
-  - 863 idle cycles: The number of cycles for which the NPU was idle.
+  - 342 idle cycles: The number of cycles for which the NPU was idle.
 
-  - 13,520,864 AXI0 read beats: The number of AXI beats with read transactions from the AXI0 bus. AXI0 is the bus where
+  - 8,895,431 AXI0 read beats: The number of AXI beats with read transactions from the AXI0 bus. AXI0 is the bus where
     the *Ethos-U* NPU reads and writes to the computation buffers, activation buf, or tensor arenas.
 
-  - 2,841,970 AXI0 write beats: The number of AXI beats with write transactions to AXI0 bus.
+  - 1,890,168 AXI0 write beats: The number of AXI beats with write transactions to AXI0 bus.
 
-  - 2,717,670 AXI1 read beats: The number of AXI beats with read transactions from the AXI1 bus. AXI1 is the bus where
+  - 1,740,069 AXI1 read beats: The number of AXI beats with read transactions from the AXI1 bus. AXI1 is the bus where
     the *Ethos-U55* NPU reads the model. So, read-only.
 
 - For FPGA platforms, a CPU cycle count can also be enabled. However, do not use cycle counters for FVP, as the CPU
   model is not cycle-approximate or cycle-accurate.
 
-> **Note:** In this example, the KWS inference does *not* use the *Ethos-U55* and only runs on the CPU. Therefore, `0`
-> Active NPU cycles are shown.
+