MLECO-3006: Fixing some minor errors in documentation
Change-Id: I24cd544780f46fcec8f154b440f7bb959c20a459
Signed-off-by: Isabella Gottardi <isabella.gottardi@arm.com>
diff --git a/docs/documentation.md b/docs/documentation.md
index f911cff..9a00cc4 100644
--- a/docs/documentation.md
+++ b/docs/documentation.md
@@ -203,10 +203,12 @@
through `CMSIS_SRC_PATH` variable.
The static library is used by platform code.
-- `components` directory contains drivers code for different devices used in platforms. Such as UART, LCD and others.
- A platform can include those as sources in a build to enable usage of corresponding HW devices. Most of the use-cases
- use UART and LCD, thus if you want to run default ML use-cases on a custom platform, you will have to add
- implementation for your devices here (or re-use existing code if it is compatible with your platform).
+- `components` directory contains drivers for different modules that can be reused for different platforms.
+ These contain common functions for Arm Ethos-U NPU initialization, timing adapter block helpers and others.
+ Each component produces a static library that could potentially be linked into the platform library to enable
+ usage of corresponding modules from the platform sources. For example, most of the use-cases use NPU and
+ timing adapter initialization. If you want to run default ML use-cases on a custom platform, you could re-use
+ existing code from this directory provided it is compatible with your platform.
- `platform/mps3`\
`platform/simple`:
@@ -228,18 +230,22 @@
Native profile allows to build application to be executed on a build machine, i.e. x86. It bypasses and stubs platform
devices replacing them with standard C or C++ library calls.
-- `platforms/bare-metal/bsp/mem_layout`: Contains the platform-specific linker scripts.
-
## Models and resources
-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).
+The models used in the use-cases implemented in this project can be downloaded from:
-- [Mobilenet V2](https://github.com/ARM-software/ML-zoo/tree/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_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)
-- [RNNoise](https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/rnnoise_INT8.tflite)
+- [Arm ML-Zoo](https://github.com/ARM-software/ML-zoo) ( [Apache 2.0 License](https://github.com/ARM-software/ML-zoo/blob/master/LICENSE) )
+
+ - [Mobilenet V2](https://github.com/ARM-software/ML-zoo/tree/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_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)
+ - [RNNoise](https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/rnnoise_INT8.tflite)
+
+- [Emza Visual Sense ModelZoo](https://github.com/emza-vs/ModelZoo) ( [Apache 2.0 License](https://github.com/emza-vs/ModelZoo/blob/v1.0/LICENSE) )
+
+ - [YOLO Fastest](https://github.com/emza-vs/ModelZoo/blob/v1.0/object_detection/yolo-fastest_192_face_v4.tflite)
When using *Ethos-U* NPU backend, Vela compiler optimizes the the NN model. However, if not and it is supported by
TensorFlow Lite Micro, then it falls back on the CPU and execute.
diff --git a/docs/sections/building.md b/docs/sections/building.md
index 2f122f9..4f4e6dd 100644
--- a/docs/sections/building.md
+++ b/docs/sections/building.md
@@ -184,7 +184,7 @@
- `Sram_Only`
> **Note:** The `Shared_Sram` memory mode is available on both *Ethos-U55* and *Ethos-U65* NPU, `Dedicated_Sram` only
- > for *Ethos-U65* NPU and `Sram_Only` only for Ethos-U55* NPU.
+ > for *Ethos-U65* NPU and `Sram_Only` only for *Ethos-U55* NPU.
- `ETHOS_U_NPU_CONFIG_ID`: This parameter is set by default based on the value of `ETHOS_U_NPU_ID`.
For Ethos-U55, it defaults to the `H128` indicating that the Ethos-U55 128 MAC optimised model
@@ -259,7 +259,7 @@
- Some files such as neural network models, network inputs, and output labels are automatically converted into C/C++
arrays, see: [Automatic file generation](./building.md#automatic-file-generation).
-3. Build the application.\
+3. Build the application.
Application and third-party libraries are now built. For further information, see:
[Building the configured project](./building.md#building-the-configured-project).
@@ -271,12 +271,12 @@
repository to link against.
1. [TensorFlow Lite Micro repository](https://github.com/tensorflow/tensorflow)
-2. [Ethos-U55 NPU core driver repository](https://review.mlplatform.org/admin/repos/ml/ethos-u/ethos-u-core-driver)
+2. [Ethos-U NPU core driver repository](https://review.mlplatform.org/admin/repos/ml/ethos-u/ethos-u-core-driver)
3. [CMSIS-5](https://github.com/ARM-software/CMSIS_5.git)
+4. [Ethos-U NPU core driver repository](https://review.mlplatform.org/admin/repos/ml/ethos-u/ethos-u-core-platform)
> **Note:** If you are using non git project sources, run `python3 ./download_dependencies.py` and ignore further git
> instructions. Proceed to [Fetching resource files](./building.md#fetching-resource-files) section.
->
To pull the submodules:
@@ -290,7 +290,7 @@
dependencies
├── cmsis
├── core-driver
- ├── core-software
+ ├── core-platform
└── tensorflow
```
@@ -391,7 +391,7 @@
#### Using GNU Arm Embedded toolchain
On Linux, if using `Arm GNU embedded toolchain`, execute the following command to build the application to run on the
-Arm® *Ethos™-U55* NPU when providing only the mandatory arguments for CMake configuration:
+Arm® *Ethos™-U* NPU when providing only the mandatory arguments for CMake configuration:
```commandline
cmake ../
diff --git a/docs/sections/customizing.md b/docs/sections/customizing.md
index ef90e5e..2302809 100644
--- a/docs/sections/customizing.md
+++ b/docs/sections/customizing.md
@@ -21,7 +21,7 @@
This section describes how to implement a custom Machine Learning application running on Arm® *Corstone™-300* based FVP
or on the Arm® MPS3 FPGA prototyping board.
-the Arm® *Ethos™-U55* code sample software project offers a way to incorporate more use-case code into the existing
+The Arm® *Ethos™-U* code sample software project offers a way to incorporate more use-case code into the existing
infrastructure. It also provides a build system that automatically picks up added functionality and produces
corresponding executable for each use-case. This is achieved by following certain configuration and code implementation
conventions.
@@ -679,7 +679,7 @@
> - `use_case` – The name of the current use-case.
> - `UC_SRC` – A list of use-case sources.
> - `UC_INCLUDE` – The path to the use-case headers.
-> - `ETHOS_U_NPU_ENABLED` – The flag indicating if the current build supports Ethos-U55.
+> - `ETHOS_U_NPU_ENABLED` – The flag indicating if the current build supports *Ethos™-U* NPU.
> - `TARGET_PLATFORM` – The target platform being built for.
> - `TARGET_SUBSYSTEM` – If target platform supports multiple subsystems, this is the name of the subsystem.
> - All standard build options.
@@ -691,9 +691,9 @@
```cmake
if (ETHOS_U_NPU_ENABLED)
- set(DEFAULT_MODEL_PATH ${DEFAULT_MODEL_DIR}/helloworldmodel_uint8_vela_${DEFAULT_NPU_CONFIG_ID}.tflite)
+ set(DEFAULT_MODEL_PATH ${DEFAULT_MODEL_DIR}/helloworldmodel_vela_${DEFAULT_NPU_CONFIG_ID}.tflite)
else()
- set(DEFAULT_MODEL_PATH ${DEFAULT_MODEL_DIR}/helloworldmodel_uint8.tflite)
+ set(DEFAULT_MODEL_PATH ${DEFAULT_MODEL_DIR}/helloworldmodel.tflite)
endif()
```
diff --git a/docs/sections/deployment.md b/docs/sections/deployment.md
index 034fb19..045bda0 100644
--- a/docs/sections/deployment.md
+++ b/docs/sections/deployment.md
@@ -25,7 +25,7 @@
Please ensure that you download the correct archive from the list under Arm® *Corstone™-300*. You need the one which:
- Emulates MPS3 board and *not* for MPS2 FPGA board,
-- Contains support for Arm® *Ethos™-U55*.
+- Contains support for Arm® *Ethos™-U55* and *Ethos-U65* processors.
### Setting up the MPS3 Arm Corstone-300 FVP
diff --git a/docs/sections/troubleshooting.md b/docs/sections/troubleshooting.md
index 612e40e..8b2646a 100644
--- a/docs/sections/troubleshooting.md
+++ b/docs/sections/troubleshooting.md
@@ -42,7 +42,7 @@
The Vela configuration parameter `accelerator-config` used for producing the .`tflite` file that is used
while building the application should match the MACs configuration that the FVP is emulating.
For example, if the `accelerator-config` from the Vela command was `ethos-u55-128`, the FVP should be emulating the
-128 MACs configuration of the Ethos-U55 block(default FVP configuration). If the `accelerator-config` used was
+128 MACs configuration of the Ethos™-U55 block(default FVP configuration). If the `accelerator-config` used was
`ethos-u55-256`, the FVP must be executed with additional command line parameter to instruct it to emulate the
256 MACs configuration instead.
@@ -56,8 +56,8 @@
INFO - MPS3 core clock has been set to: 32000000Hz
INFO - CPU ID: 0x410fd220
INFO - CPU: Cortex-M55 r0p0
-INFO - Ethos-U55 device initialised
-INFO - Ethos-U55 version info:
+INFO - Ethos-U device initialised
+INFO - Ethos-U version info:
INFO - Arch: v1.0.6
INFO - Driver: v0.16.0
INFO - MACs/cc: 128
diff --git a/docs/use_cases/asr.md b/docs/use_cases/asr.md
index 46ef584..adeb838 100644
--- a/docs/use_cases/asr.md
+++ b/docs/use_cases/asr.md
@@ -346,8 +346,7 @@
using:
```commandline
-~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55
-./bin/mps3-sse-300/ethos-u-asr.axf
+~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55 ./bin/mps3-sse-300/ethos-u-asr.axf
```
A log output appears on the terminal:
diff --git a/docs/use_cases/img_class.md b/docs/use_cases/img_class.md
index 494ec61..7db6e39 100644
--- a/docs/use_cases/img_class.md
+++ b/docs/use_cases/img_class.md
@@ -269,8 +269,7 @@
using:
```commandline
-~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55
-./bin/mps3-sse-300/ethos-u-img_class.axf
+~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55 ./bin/mps3-sse-300/ethos-u-img_class.axf
```
A log output appears on the terminal:
diff --git a/docs/use_cases/inference_runner.md b/docs/use_cases/inference_runner.md
index 0aa671a..1082c5c 100644
--- a/docs/use_cases/inference_runner.md
+++ b/docs/use_cases/inference_runner.md
@@ -205,8 +205,7 @@
using:
```commandline
-~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55
-./bin/mps3-sse-300/ethos-u-inference_runner.axf
+~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55 -a ./bin/mps3-sse-300/ethos-u-inference_runner.axf
```
A log output appears on the terminal:
@@ -309,8 +308,8 @@
> the model size can be a maximum of 32MiB. The IFM and OFM spaces are both reserved as 16MiB sections.
```commandline
-~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55 -a \
- ./bin/ethos-u-inference_runner.axf \
+~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55 \
+ -a ./bin/ethos-u-inference_runner.axf \
--data /path/to/custom-model.tflite@0x90000000 \
--data /path/to/custom-ifm.bin@0x92000000 \
--dump cpu0=/path/to/output.bin@Memory:0x93000000,1024
diff --git a/docs/use_cases/kws.md b/docs/use_cases/kws.md
index d07dff2..bda22bf 100644
--- a/docs/use_cases/kws.md
+++ b/docs/use_cases/kws.md
@@ -313,8 +313,7 @@
using:
```commandline
-~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55
-./bin/mps3-sse-300/ethos-u-kws.axf
+~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55 ./bin/mps3-sse-300/ethos-u-kws.axf
```
A log output appears on the terminal:
diff --git a/docs/use_cases/kws_asr.md b/docs/use_cases/kws_asr.md
index 8013634..d8b2fee 100644
--- a/docs/use_cases/kws_asr.md
+++ b/docs/use_cases/kws_asr.md
@@ -404,8 +404,7 @@
using:
```commandline
-$ ~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55
-./bin/mps3-sse-300/ethos-u-kws_asr.axf
+$ ~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55 ./bin/mps3-sse-300/ethos-u-kws_asr.axf
```
A log output appears on the terminal:
diff --git a/docs/use_cases/visual_wake_word.md b/docs/use_cases/visual_wake_word.md
index a6f6130..99aa3f2 100644
--- a/docs/use_cases/visual_wake_word.md
+++ b/docs/use_cases/visual_wake_word.md
@@ -249,8 +249,7 @@
package. Assuming the install location of the FVP was set to ~/FVP_install_location, the simulation can be started by:
```commandline
-$ ~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55
-./bin/mps3-sse-300/ethos-u-vww.axf
+$ ~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55 ./bin/mps3-sse-300/ethos-u-vww.axf
```
A log output should appear on the terminal:
diff --git a/model_conditioning_examples/Readme.md b/model_conditioning_examples/Readme.md
index bb00b79..9bfc968 100644
--- a/model_conditioning_examples/Readme.md
+++ b/model_conditioning_examples/Readme.md
@@ -12,7 +12,7 @@
## Introduction
This folder contains short example scripts that demonstrate some methods available in TensorFlow to condition your model
-in preparation for deployment on Arm Ethos NPU.
+in preparation for deployment on Arm® Ethos™ NPU.
These scripts will cover three main topics:
@@ -22,7 +22,7 @@
The objective of these scripts is not to be a single source of knowledge on everything related to model conditioning.
Instead the aim is to provide the reader with a quick starting point that demonstrates some commonly used tools that
-will enable models to run on Arm Ethos NPU and also optimize them to enable maximum performance from the Arm Ethos NPU.
+will enable models to run on Arm® Ethos-U NPU and also optimize them to enable maximum performance from the Arm® Ethos-U NPU.
Links to more in-depth guides available on the TensorFlow website are provided in the [references](#references) section
in this Readme.
@@ -54,8 +54,8 @@
## Quantization
-Most machine learning models are trained using 32bit floating point precision. However, Arm Ethos NPU performs
-calculations in 8bit integer precision. As a result, it is required that any model you wish to deploy on Arm Ethos NPU is
+Most machine learning models are trained using 32bit floating point precision. However, Arm® Ethos-U NPU performs
+calculations in 8bit integer precision. As a result, it is required that any model you wish to deploy on Arm® Ethos-U NPU is
first fully quantized to 8bits.
TensorFlow provides two methods of quantization and the scripts in this folder will demonstrate these:
@@ -94,7 +94,7 @@
drop when using post-training quantization is usually minimal. After post-training quantization is complete you will
have a fully quantized TensorFlow Lite model.
-If you are targetting an Arm Ethos-U55 NPU then the output TensorFlow Lite file will also need to be passed through the Vela
+If you are targetting an Arm® Ethos-U NPU then the output TensorFlow Lite file will also need to be passed through the Vela
compiler for further optimizations before it can be used.
### Quantization aware training
@@ -117,7 +117,7 @@
model can be fully quantized afterwards. Once you have finished quantization aware training the TensorFlow Lite converter is
used to produce a fully quantized TensorFlow Lite model.
-If you are targetting an Arm Ethos-U55 NPU then the output TensorFlow Lite file will also need to be passed through the Vela
+If you are targetting an Arm® Ethos-U NPU then the output TensorFlow Lite file will also need to be passed through the Vela
compiler for further optimizations before it can be used.
## Weight pruning
@@ -128,7 +128,7 @@
values to 0, resulting in a sparse model.
Compression algorithms can then take advantage of this to reduce model size in memory, which can be very important when
-deploying on small embedded systems. Moreover, Arm Ethos NPU can take advantage of model sparsity to further accelerate
+deploying on small embedded systems. Moreover, Arm® Ethos-U NPU can take advantage of model sparsity to further accelerate
execution of a model.
Training with weight pruning will force your model to have a certain percentage of its weights set (or 'pruned') to 0
@@ -139,9 +139,9 @@
Weight pruning can be further combined with quantization so you have a model that is both pruned and quantized, meaning
that the memory saving affects of both can be combined. Quantization then allows the model to be used with
-Arm Ethos NPU.
+Arm® Ethos-U NPU.
-If you are targetting an Arm Ethos-U55 NPU then the output TensorFlow Lite file will also need to be passed through the Vela
+If you are targetting an Arm® Ethos-U NPU then the output TensorFlow Lite file will also need to be passed through the Vela
compiler for further optimizations before it can be used.
## Weight clustering
@@ -158,9 +158,9 @@
Weight clustering can be further combined with quantization so you have a model that is both clustered and quantized,
meaning that the memory saving affects of both can be combined. Quantization then allows the model to be used with
-Arm Ethos NPU.
+Arm® Ethos-U NPU.
-If you are targetting an Arm Ethos-U55 NPU then the output TensorFlow Lite file will also need to be passed through the Vela
+If you are targetting an Arm® Ethos-U NPU then the output TensorFlow Lite file will also need to be passed through the Vela
compiler for further optimizations before it can be used (see [Optimize model with Vela compiler](./building.md#optimize-custom-model-with-vela-compiler)).
## References