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python tools/python/convert.py --config=mace.yml
--target_abi=armeabi-v7a
--target_socs=myriad
--device_conf=myriad
--enable_opencl=true
scp mace-cl-compiled-program.bin user@raspberrypi:/home/user/model/
ssh user@raspberrypi
cd /home/user/model
./my_mace_app --program mace-cl-compiled-program.bin
with open("mace-cl-compiled-program.bin", "rb") as f: prog_bin = f.read()
| Error | Likely Cause | Fix |
|-------|--------------|-----|
| CL_INVALID_BINARY | Wrong device/target | Recompile for correct SoC |
| MACE_OUT_OF_RESOURCES | Binary too large | Reduce kernel count or split model |
| Magic number mismatch | Corrupted file | Regenerate binary |
| Version mismatch | MACE runtime older than compiler | Upgrade MACE runtime |
import mace
In the world of mobile and edge computing, performance is paramount. However, raw processing power is useless if the overhead of compiling code eats into your frame budget. This is where cryptic cache files like mace-cl-compiled-program.bin come into play.
If you are a mobile developer, a digital forensics expert, or a power user digging through the file system of an Android device, you have likely encountered this file. To the untrained eye, it looks like random binary noise. To a specialist, it is evidence of a sophisticated neural network runtime at work.
This article dissects mace-cl-compiled-program.bin—what it is, why it exists, how it works, and whether you should ever touch it.
The mace-cl-compiled-program.bin file is a compiled machine learning model designed for efficient execution on mobile and embedded devices using the MACE framework. Understanding its purpose and how to work with it can help developers and engineers deploy AI capabilities on a wide range of devices.
| Attribute | Value |
|-----------|-------|
| Framework | MACE (Mobile AI Compute Engine, by Xiaomi) |
| Language | OpenCL C (pre-compiled to binary) |
| Hardware target | Specific GPU model (e.g., Adreno, Mali, PowerVR) |
| Purpose | Accelerate neural network inference on GPU without JIT compilation time |
| Portability | None – crashes on different GPU |
| Typical location | App’s assets or model cache directory |
The file mace_cl_compiled_program.bin is a binary cache file generated by the Mobile AI Compute Engine (MACE) framework. It contains pre-compiled OpenCL kernels specifically optimized for a device's GPU. Purpose and Functionality
In mobile AI inference, compiling OpenCL kernels at runtime (JIT compilation) can be a slow process, sometimes taking several seconds. This file solves that by storing the compiled binary so it can be reused in future sessions:
Initialization Speed: By loading this pre-compiled binary, the MACE Engine skips the compilation step, drastically reducing the startup time for machine learning models on mobile devices.
Performance Tuning: MACE can automatically tune GPU parameters for specific System-on-Chips (SoCs). These tuned parameters and kernels are then stored in this .bin file to ensure the best possible performance. Key Characteristics
Device Dependency: The content of this file is strictly tied to the OpenCL version and the specific GPU architecture of the device that generated it. A binary generated on one phone (e.g., a Qualcomm Snapdragon device) will typically not be compatible with a different SoC. mace-cl-compiled-program.bin
Generation: It is typically created during the first run of a MACE-powered application or during an offline "tuning" process where the model is benchmarked on the target hardware.
Typical Path: On Android devices, applications using MACE often look for or save this file in the app's internal data directory or public storage (e.g., /storage/emulated/0/mace_cl_compiled_program.bin). Common Issues How to build - MACE documentation - Read the Docs
The file "mace-cl-compiled-program.bin" is a binary file generated by Xiaomi's MACE (Mobile AI Compute Engine), a deep learning inference framework optimized for mobile devices. This specific file contains compiled OpenCL kernels, which are small programs that allow AI models to run efficiently on a device's GPU.
Generating this "piece" or file is typically done during the deployment phase to avoid slow runtime compilation. How to Generate "mace-cl-compiled-program.bin"
To create this file, you must use the MACE converter tool to process your model for a specific target device.
Configure a Deployment YAML: Create a .yml file that defines your model and specifies the gpu as a runtime.
Specify Target SOCs: Ensure your configuration includes the target_socs (System-on-Chip) relevant to the device you are targeting.
Run the MACE Converter: Execute the converter.py tool. When the gpu runtime and target SOCs are correctly specified, MACE automatically compiles the OpenCL kernels for that hardware.
Extract the Binary: The compilation process produces the binary file (often named $library_name_compiled_opencl_kernel.$device_name.$soc.bin), which you can then rename or use as the required mace-cl-compiled-program.bin in your project. Key Considerations
Hardware Dependency: These binaries are device-specific. A file compiled for a Snapdragon 835 may not work on a Snapdragon 888 because the GPU architectures differ.
Performance: Using a pre-compiled .bin file significantly reduces the "first-run" latency of an AI application by skipping the kernel compilation step at startup.
Troubleshooting: If the file is missing, the MACE engine may try to compile kernels at runtime, which can cause significant delays or crashes if the OpenCL environment is not properly set up. python tools/python/convert
Are you trying to deploy a specific AI model (like MobileNet) to an Android device using MACE? How to build - MACE documentation - Read the Docs
The file mace-cl-compiled-program.bin is a binary file generated by the Mobile AI Compute Engine (MACE), a deep learning inference framework developed by Xiaomi. This specific file contains compiled OpenCL kernels and tuned parameters optimized for a specific mobile device's GPU.
Since you are looking to put together a "proper report" based on this technical artifact, the report should focus on the deployment and performance tuning of an AI model on mobile hardware. 1. Project Overview Framework: Mobile AI Compute Engine (MACE)
Target Device: Identify the specific mobile SoC (System on Chip) used, such as a Snapdragon 660.
Model Name: Specify the model being deployed (e.g., Inception-v3, MobileNet-v2). 2. Technical Summary of the Binary File File Name: mace_cl_compiled_program.bin
Purpose: Stores compiled OpenCL kernels to reduce model initialization time (warm-up time) by avoiding recompilation on every run.
Generation: Created during the "tuning" phase when running a model with the --gencode or tuning flags.
Location: Typically found in /data/local/tmp/mace_run/ on the target Android device. 3. Deployment Workflow
Environment Setup: Ensure the MACE environment is configured as per the Official Documentation.
Model Conversion: Convert the original model (e.g., TensorFlow or PyTorch) into the MACE format.
Tuning: Execute a tuning run on the target device to generate the .bin file. This optimizes the OpenCL kernels for the specific GPU architecture.
Validation: Use the MACE python validation scripts to verify that the compiled model maintains accuracy (e.g., Top-1 accuracy). 4. Performance & Validation Results with open("mace-cl-compiled-program
Initialization Time: Compare model start-up time with and without the mace_cl_compiled_program.bin file.
Inference Latency: Record the average time per inference (in milliseconds) on the GPU.
Accuracy Metrics: State the accuracy results from the validation plugin to ensure the quantization or compilation didn't degrade performance. 5. Troubleshooting & Maintenance
Failure to Read: If the engine fails to read the tuned parameters, ensure the file path in mace_run.config matches the actual location on the device.
Device Dependency: Note that this binary is device-specific; a file generated for a Snapdragon 660 will not work optimally on a different chipset.
In the context of the Xiaomi MACE (Mobile AI Compute Engine) framework, mace_cl_compiled_program.bin is a pre-compiled OpenCL binary cache file. Function and Purpose
Performance Optimization: This file stores compiled OpenCL kernels specifically tuned for a device's GPU. By caching these kernels, MACE avoids the overhead of recompiling them every time an application starts, which significantly reduces the initialization time of the AI engine.
Hardware Targeting: Because OpenCL kernels are hardware-dependent, this .bin file is typically generated at runtime on the target device (like a smartphone) to match its specific GPU architecture.
GPU Tuning: The file is often related to the Tuning feature in MACE, which benchmarks and selects the best OpenCL parameters for a specific System-on-Chip (SoC). Common Technical Details
Location: On Android devices, it is frequently found in application-specific storage paths or external storage (e.g., /storage/emulated/0/mace_cl_compiled_program.bin).
Troubleshooting: If the file is missing, MACE will log a message stating "There is no precompiled OpenCL binary" and fall back to on-the-fly compilation, which may result in a slower first-run experience.
Security: Developers can use the MACE Model Protection features to manage how model data and binaries are stored on embedded devices.
You can explore the source code for this inference framework at the Xiaomi MACE GitHub repository. For general open-source security practices related to such libraries, tools from the OWASP Foundation can be utilized. Additionally, organizational strategies for digital transformation using open-source tools are discussed in the Presans Whitepaper on Open Organizations.
Are you trying to fix a "file not found" error in a MACE-based app, or are you integrating GPU tuning into your own project?