Hello,
I am encountering an issue when converting a TensorFlow Dense layer to .tflite and subsequently using the ST Edge AI tool to convert it to .nb format, following the ST Edge AI Guide for MPU.
When using a lower-dimensional input, the conversion succeeds. For example, the following code works as expected:
input_features = tf.keras.Input(shape=(1, 384), dtype=tf.float32)
output = tf.keras.layers.Dense(1536)(input_features)
model = tf.keras.Model(inputs=input_features, outputs=output)
However, increasing the input dimension to (1500, 384) results in the following error:
ST Edge AI Core v2.0.0-20049
PASS: 0%| | 0/2 [00:00<?, ?it/s]
Galcore warning: MMU is disabled!
E [main.c:vnn_VerifyGraph:93]CHECK STATUS(-3:The requested set of parameters produce a configuration that cannot be supported.)
E [main.c:main:236]CHECK STATUS(-3:The requested set of parameters produce a configuration that cannot be supported.)
E 21:37:15 Fatal model generation error: 64768
E010(InvalidModelError): Error during NBG compilation, model is not supported
Is there a known limitation on the input/output dimensions for Dense layers when converting to .nb format? If so, is there a recommended approach to handle larger dimensions, or any workarounds to enable successful conversion and NPU acceleration?
Thank you for your time and assistance.
Best regards,
Justin
