DevHeads IoT Integration Server•2mo ago

How to Resolve RuntimeError in TensorFlow Lite for Microcontrollers on ESP32?

Hello everyone, I'm deploying a natural language processing (NLP) model on an ESP32 microcontroller using TensorFlow Lite for Microcontrollers. During inference, I encounter the error RuntimeError: Internal error: Failed to run model at node 13 with status 1. This error seems to occur randomly during different stages of text processing, particularly when processing longer input sequences. I've already applied model quantization, reduced the complexity of the input text, and ensured that the input data is properly formatted. What advanced debugging techniques or memory management strategies can I apply to resolve this runtime error? Are there specific configurations in TensorFlow Lite or best practices for managing memory on the ESP32 that can help improve the stability of NLP models?

4 Replies

Joseph Ogbonna•2mo ago

Try the following: 1. Increase Heap Size: Ensure enough memory is allocated. 2. Optimize the Model: Use int8 quantization and consider pruning to reduce model size. 3. Simplify Input: Reduce the length of input sequences. 4. Check for Unsupported Operations: Ensure all operations are supported by TensorFlow Lite for Microcontrollers. These steps should help stabilize your model's performance on the ESP32.

Joseph Ogbonna•2mo ago

Also check out this library for Tensorflow and MCU https://github.com/tensorflow/tflite-micro

GitHub

GitHub - tensorflow/tflite-micro: Infrastructure to enable deployme...

Infrastructure to enable deployment of ML models to low-power resource-constrained embedded targets (including microcontrollers and digital signal processors). - tensorflow/tflite-micro

RED HAT•2mo ago

One thing you can do is enable verbose logging in TensorFlow Lite to get more detailed error messages. This might help you identify which part of the model or which operation is causing the issue. You can enable verbose logging by setting the TF_LITE_REPORT_ERROR macro in your code. Here's how you can do it:

#define TF_LITE_REPORT_ERROR(reporter, ...) \
  do { \
    printf(__VA_ARGS__); \
    printf("\n"); \
  } while (0)

#define TF_LITE_REPORT_ERROR(reporter, ...) \
  do { \
    printf(__VA_ARGS__); \
    printf("\n"); \
  } while (0)

wafa_ath•2mo ago

Thank you for the suggestion! I've enabled verbose logging using the TF_LITE_REPORT_ERROR macro and here's the detailed error message I received:

Model inference started...
Node 1: Custom Op1 executed successfully
Node 2: Conv2D executed successfully
Node 3: DepthwiseConv2D executed successfully
Node 4: FullyConnected executed successfully
Node 5: Custom Op2 executed successfully
...
Node 12: Softmax executed successfully
Node 13: LSTM encountered an error
Error: Internal error: Failed to run model at node 13 with status 1
  TensorFlow Lite Micro Interpreter Error:
    Node type: LSTM
    Node ID: 13
    Status: kTfLiteError (1)
    Error message: Memory allocation failed
  Additional details:
    Input tensor: Shape [1, 128]
    Output tensor: Shape [1, 64]
    Activation tensor: Shape [1, 256]
    Total required memory: 2048 bytes
    Available memory: 1024 bytes
Model inference terminated with error.

Model inference started...
Node 1: Custom Op1 executed successfully
Node 2: Conv2D executed successfully
Node 3: DepthwiseConv2D executed successfully
Node 4: FullyConnected executed successfully
Node 5: Custom Op2 executed successfully
...
Node 12: Softmax executed successfully
Node 13: LSTM encountered an error
Error: Internal error: Failed to run model at node 13 with status 1
  TensorFlow Lite Micro Interpreter Error:
    Node type: LSTM
    Node ID: 13
    Status: kTfLiteError (1)
    Error message: Memory allocation failed
  Additional details:
    Input tensor: Shape [1, 128]
    Output tensor: Shape [1, 64]
    Activation tensor: Shape [1, 256]
    Total required memory: 2048 bytes
    Available memory: 1024 bytes
Model inference terminated with error.

It seems like the LSTM node is encountering a memory allocation problem. What Are there specific memory management strategies or configurations in TensorFlow Lite that could help improve the stability of NLP models on the ESP32? Thanks again for your help

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How to Resolve RuntimeError in TensorFlow Lite for Microcontrollers on ESP32?