Embedded AI Engine · TFLite Compatible · C99
A TensorFlow Lite-compatible inference engine implemented in C99. Enables AI inference in NonOS/RTOS environments where the official TFLite does not work. Supports NPU acceleration via NNAPI on Android, and fast Int8 model processing on both x86 and Arm.
Challenges & Solutions
Common challenges in embedded AI development, and how ailia TFLite solves them.
Challenges with Official TFLite / Vendor SDKs
C++ dependency prevents use in NonOS or RTOS environments
Vendor SDKs require model conversion → risk of conversion errors and accuracy loss
Official TFLite Int8 is not x86-optimized → slow even on PC
Graphs with NPU-unsupported layers (e.g. NonMaxSuppression) cannot run
C++ memory management is poorly suited for embedded memory constraints
ailia TFLite Solutions
C99 implementation. Runs in any embedded environment including NonOS and RTOS
Parses .tflite directly at runtime. No conversion, no accuracy loss, no errors
Proprietary optimizations deliver major x86 Int8 inference speedups
Auto subgraph partitioning via NNAPI. NPU-unsupported layers offloaded to CPU
C API accepts custom memory allocators. Fully compatible with embedded memory management
Detailed Comparison
A technical feature comparison for embedded AI deployment.
| Feature / Characteristic | ailia TFLite |
Official TensorFlow Lite |
Device Vendor SDK |
|---|---|---|---|
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NonOS / RTOS Support
AI inference without OS or on RTOS
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Automatic Subgraph Partitioning
Offloads NPU-unsupported layers to CPU
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C API Custom Memory Management
Specify custom allocator for embedded use
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Device-dependent |
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No Model Conversion
Run .tflite as-is at runtime
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x86 Int8 Acceleration (MKL)
Fast Int8 inference on PC environments
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TFLite-Compatible Python API
Migrate by changing just one import line
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NPU Inference (NNAPI)
Fast inference using NPU on Android
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Partial |
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Available as Unity Package
Integrate NPU inference into Unity apps
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Key Features
Pure C99 implementation with no C++ dependency. Enables AI inference in NonOS and RTOS environments where official TFLite cannot run.
C99 / RTOS SupportUnlike vendor SDKs, parses .tflite files directly at runtime. Eliminates the risk of accuracy loss and conversion errors entirely.
Zero Conversion CostRuns NPU inference via Android NNAPI. Even complex graphs with NNAPI-unsupported layers (e.g. NonMaxSuppression) complete fully using automatic NPU+CPU subgraph partitioning.
NPU / NNAPIx86-optimized implementation using Intel MKL delivers major speedups for Int8 model inference that official TFLite leaves unoptimized. Also useful for PC-based prototyping.
x86 MKL OptimizedFully compatible with the official TFLite Python API. Replace existing code with ailia TFLite by changing just one import line. The C API supports custom memory allocators.
Supports both Float and Int8 models. Using Int8 reduces memory usage to approximately 1/4. Example with ResNet50: Float (102.2MB) → Int8 (26.3MB). Ideal for memory-constrained embedded devices.
1/4 Memory UsageCode Sample
Fully compatible with the official TFLite Python Interpreter API. The only change to existing code is one import line.
# Official TFLite import from tensorflow.lite.python.interpreter import Interpreter interpreter = Interpreter( model_path="face_detection_front.tflite" ) interpreter.allocate_tensors() input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() interpreter.set_tensor( input_details[0]['index'], input_data ) interpreter.invoke() output = interpreter.get_tensor( output_details[0]['index'] )
# ← Only this line changes (1 line) from ailia_tflite import Interpreter interpreter = Interpreter( model_path="face_detection_front.tflite" ) interpreter.allocate_tensors() input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() interpreter.set_tensor( input_details[0]['index'], input_data ) interpreter.invoke() output = interpreter.get_tensor( output_details[0]['index'] )
allocate_tensors(), set_tensor(), invoke(), and get_tensor() are all fully compatible with official TFLite. No changes to your business logic are needed.
Supported Platforms
Consistent API coverage from embedded to mobile.
C99 implementation for OS-free and RTOS embedded environments. Enables AI inference on resource-constrained devices.
Fast NPU inference via NNAPI. Complex graphs like YOLOX work via subgraph partitioning. Available as a Unity Package.
Python API for PC environments. x86 acceleration via Intel MKL. Suitable from development and prototyping to production.
Use Cases
Real-world use cases where official TFLite falls short or NPU inference is required.
Embedded Device · NonOS / RTOS
Integrates AI into NonOS/RTOS environments where official TFLite cannot run, using ailia TFLite's C99 implementation. Runs .tflite directly without conversion, eliminating conversion error risks.
Android App · NPU Inference
ailia TFLite's NNAPI support enables real-time inference using NPUs on MediaTek and Qualcomm devices. Complex graphs like YOLOX work via subgraph partitioning. Up to 15x faster than CPU.
PC / Server · x86 Acceleration
Dramatically accelerates x86 Int8 inference that official TFLite leaves unoptimized, using Intel MKL. Shared API with embedded targets makes porting from PC to device seamless.
Purchase & Licensing
The evaluation version lets you test with your own .tflite model right away. No per-inference charges.
Tier 01
Free · No credit card required
All features available during evaluationPython and C API both supportedWindows / macOS / Linux supportedReady to test with ailia-models-tflite sample models
Commercial distribution / production release PoC delivery to third partiesTier 02
Pricing tailored to your use case and device count.
Integration into production and commercial productsRight to redistribute to end usersBundled contracts with ailia SDK availableEmail and meeting supportDedicated team support in Japanese and EnglishWorking with ONNX models?
If you need fast inference via Vulkan / Metal GPU or access to 400+ ONNX models, consider ailia SDK.
View ailia SDK (ONNX) →Get Started
No conversion needed. Test with your own .tflite model instantly. Install via pip and change just one import line.
Works with both Float and Int8 models. No conversion required.
pip install ailia_tflite — Installation completes in seconds.
Simply change to from ailia_tflite import Interpreter. No other code changes needed.
Many sample models are available at ailia-models-tflite.
Tell us your use case, device count, and distribution model. Consultation and quotes are free.
