About This Mac in OS X 10.11 El Capitan About This Mac is a menu command found under all versions of Mac OS X. It resides under the Apple menu as the first menu item; the only exception was during the period of the Mac OS X Public Beta, when it resided in the Desktop menu. Cortex Command 1.05 Mac Native Uploaded, Size 56.18 MiB, ULed by boundT: 0: 0: Games Cortex Command version 1.05 ENG (2011-2013). Cortex Command b23 Cortex Command is set a few hundred years into the future.
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Download and build the sample application
Install Arm toolchain and Mbed CLI
- Download Arm cross compilation toolchain. Select the correct toolchain for the OS that your computer is running. For Windows users, if you have already set up the Linux virtual environment, install the toolchain there.
- To build and deploy the application, we use the Mbed CLI. We recommend that you install Mbed CLI with our installer. If you need more customization, you can perform a manual install. Although this is not recommended.
If you do not already have Mbed CLI installed, download the installer:
Mac installer - After Mbed CLI is installed, tell Mbed where to find the Arm embedded toolchain using the following command:
Note
We recommend running the following commands from inside the Mbed CLI terminal that gets launched with the Mbed CLI Application. This is because it is much quicker to set up, because it resolves all your environment dependencies automatically.
V2.0 auto-update issue on Mac Sierra systems solved (since release date 5.6.2018) Short upcoming AXON start screen after CORTEXpro firmware update eliminated (since release 20.6.2018) Note. You may want to check out more Mac applications, such as Cortex Command b23, Air Force Commander or Cortex, which might be similar to Cortex Command.
Build and compile micro speech example
Navigate to the directory where you keep code projects. Run the following command to download TensorFlow Lite source code.
While you wait for the project to download, let us explore the project files on GitHub and learn how this TensorFlow Lite for Microcontrollers example works.
The code samples audio from the microphone on the STM32F7. The audio is run through a Fast Fourier transform to create a spectrogram. The spectrogram is then fed into a pre-trained machine learning model. The model uses a convolutional neural network to identify whether the sample represents either the command “yes” or “no”, silence, or an unknown input. We explore how this works in more detail later in the guide.
The micro speech sample application is in the
tensorflow/lite/micro/examples/microspeech
directory.![Cortex Command For Mac Cortex Command For Mac](/uploads/1/2/6/6/126639277/724712078.jpg)
Here are descriptions of some interesting source files:
- disco_f746ng/audio_provider.cc captures audio from the microphone on the device.
- micro_features/micro_features_generator.cc: uses a Fast Fourier transform to create a spectrogram from audio.
- micro_features/tiny_conv_micro_features_model_data.cc. This file is the machine learning model itself, represented by a large array of unsigned char values.
- command_responder.cc is called every time a potential command has been identified.
- main.cc. This file is the entry point for the Mbed program, which runs the machine learning model using TensorFlow Lite for Microcontrollers.
After the project has downloaded, you can run the following commands to navigate into the project directory and build it:
These commands create an Mbed project folder in
tensorflow/lite/micro/tools/make/gen/mbed_cortex-m4/prj/micro_speech/mbed.
The micro speech source code of the generated Mbed project is in tensorflow/lite/micro/tools/make/gen/mbed_cortex-m4/prj/micro_speech/mbed/tensorflow/lite/micro/examples/micro_speech.If you must make further changes to the source code after generating the Mbed project, change the source code in the micro_speech folder.
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If you encounter the error message
'Tensorflow/lite/micro/tools/make/Makefile:2 *** “Require make version 3.82 or later (current 3.81)'
, please refer to the Troubleshootingsection.TensorFlow requires C++ 11, so you must update your profiles to reflect this. Here is a short Python command that does that. Run it from the command line:
After that setting is updated, you can compile:
CMSIS-NN
In the example above, we compiled our project with a
TAGS='cmsis-nn'
flag, which enables kernel optimization with CMSIS-NN library. Following are some CMSIS-NN acceleration techniques.The CMSIS-NN library provides optimized neural network kernel implementations for all Arm Cortex-M processors, ranging from Cortex-M0 to Cortex-M55. The library utilizes the capabilities of the processor, such as DSP and M-Profile Vector (MVE) extensions, to enable the best possible performance.
The STMicroelectronics F746NG Discovery board we use in the guide is powered by Arm Cortex-M7, which supports DSP extensions. That enables the optimized kernels to perform multiple operations in one cycle using SIMD (Single Instruction Multiple Data) instructions. Another optimization technique used by the CMSIS-NN library is loop unrolling. These techniques combined significantly accelerate kernel performance on Arm MCUs.
In the following example, we use the SIMD instruction, SMLAD (Signed Multiply with Addition), together with loop unrolling to perform a matrix multiplication y = a * b, where
and
a, b are 8-bit values and y is a 32-bit value. With regular C, the code would look something like the following code:
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However, using loop unrolling and SIMD instructions, the loop looks like the following code:
This code saves cycles due to fewer for-loop checks since
__SMLAD performs two multiply and accumulate operations in one cycle.
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With CMSIS-NN enabled, we observed a 16x performance uplift in the micro speech inference time.
After a long wait, Cortex Command for OSX is out. This is a universal binary package, so it should work on most Macs out there (as long as they are running a version of OSX higher or equal to 10.4 (Tiger))! However, we couldn’t test this on all possible combos ourselves, so if you find it doesn’t run on your mac, please send the error message to us at [email protected].
A big thank you and kudos goes to Chris Kruger who did a fantastic job getting this complex piece of software running on this great platform! Cortex Command is built on half a dozen libraries and middleware components that all had to be made to run in harmony in the Mac environment – on both old PPC and new Intel flavors. It wasn’t a walk in the park.
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We need your help to spread the word to game-starved Apple fans everywhere! If you have a friend who uses Mac and haven’t been able to play CC yet, how about getting him/her a copy of the game for the holidays?
Finally, note that if you have bought a license for Cortex Command, you can use it to play any version on any platform. In other words, the keys are platform-agnostic.
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