I have been trying out ml-embedded kit and getting used to it. I tried to deploy a custom model on MPS3 board and faced a following error.
INFO - V2M-MPS3 revision C INFO - Application Note AN552, Revision B INFO - MPS3 build 2 INFO - MPS3 core clock has been set to: 32000000Hz INFO - CPU ID: 0x411fd220 INFO - CPU: Cortex-M55 r1p0 INFO - Arm Corstone-300 - AN552 platform initialised INFO - ARM ML Embedded Evaluation Kit for MPS3 FPGA and FastModel INFO - Target system design: Arm Corstone-300 - AN552 INFO - Version 21.11 Build date: Jan 26 2022 @ 14:34:51 INFO - Copyright (C) ARM Ltd 2021. All rights reserved. INFO - Creating allocator using tensor arena in SRAM INFO - Allocating tensors Didn't find op for builtin opcode 'PAD' version '2'. An older version of this builtin might be supported. Are you using an old TFLite binary with a newer model? Failed to get registration from op code PAD [ERROR] allocateTensors() failed ERROR - tensor allocation failed! ERROR - Failed to initialise model INFO - program terminating..
I took a tf.keras model and converted it into a tflite model by the following steps.
converter = tf.lite.TFLiteConverter.from_keras_model(model) converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE] converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] converter.representative_dataset = rep_dataset converter.inference_input_type = tf.int8 converter.inference_output_type = tf.int8 tflite_model = converter.convert()
It would be helpful if I could get some insights if possible.
I’m using TF 2.5.0 to convert TF Keras model into TFLite.