How to reduce the time running invoke()
@abichoi The significant difference in inference time between your two models is expected. Kindly follow the process below to reduce the invoke() time for the SSD Mobilenet v2 FPNLite 640x640 model on your Raspberry Pi 4B:

Please enable NNAPI acceleration by setting the target option in the Interpreter creation:
```

interpreter = tf.lite.Interpreter(model_path="ssd_mobilenet_v2_fpnlite_640x640.tflite", experimental_options=tf.lite.InterpreterOptions(target=tf.lite.REFERRED_TARGETS[0]))
interpreter.allocate_tensors()

```
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### Issue type

Support

### Have you reproduced the bug with TensorFlow Nightly?

No

### Source

source

### TensorFlow version

tf 2.15

### Custom code

Yes

### OS platform and distribution

arm64 Debian GNU/Linux 11

### Mobile device

_No response_

### Python version

3.9.2

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

I have converted ssd mobilenet v2 320 x320 and ssd mobilenet v2 fpnlite 640 x 640 into tflite models with iou = 0.3 for both of them. On a raspberry pi 4B 4GB, I found out that invoke() takes 0.22 seconds to run for ssd mobilenet v2 320 x 320 and 2.5 seconds to run for ssd mobilenet v2 fpnlite 640 x 640. 
Is there a way to reduce the time needed for invoke() to run?

### Standalone code to reproduce the issue

```shell
import numpy as np
import tensorflow as tf
import cv2
import time

model_name = "ssd_mobilenet_v2_fpnlite_640x640_iou03" #conf 0.3
model_path = "MODEL_PATH"

interpreter = tf.lite.Interpreter(model_path=model_path)

# Get model details
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
height = input_details[0]['shape'][1]
width = input_details[0]['shape'][2]

floating_model = (input_details[0]['dtype'] == np.float32)

input_mean = 127.5
input_std = 127.5

interpreter.allocate_tensors()

min_conf_threshold = 0.3
image_path = "IMAGE_PATH"
frame1=cv2.imread(image_path)
imH, imW, channels = frame1.shape
frame=frame1.copy()
frame_rgb = frame
frame_resized = cv2.resize(frame_rgb, (width, height))

input_data = np.expand_dims(frame_resized, axis=0)
floating_model = (input_details[0]['dtype'] == np.float32)
if floating_model:
  input_data = (np.float32(input_data) - input_mean) / input_std

# Perform the actual detection by running the model with the image as input
interpreter.set_tensor(input_details[0]['index'],input_data)
invoke_start = time.time()
interpreter.invoke()
print(time.time()-invoke_start)

# Retrieve detection results
boxes = interpreter.get_tensor(output_details[1]['index'])[0] # Bounding box coordinates of detected objects
classes = interpreter.get_tensor(output_details[3]['index'])[0] # Class index of detected objects
scores = interpreter.get_tensor(output_details[0]['index'])[0] # Confidence of detected objects

for i in range(len(scores)):
    if ((scores[i] > min_conf_threshold) and (scores[i] <= 1.0)):
        ymin = int(max(1,((boxes[i][0]* imH))))
        xmin = int(max(1,(boxes[i][1] * imW)))
        ymax = int(min(imH,((boxes[i][2]* imH))))
        xmax = int(min(imW,(boxes[i][3] * imW)))

        cv2.rectangle(frame, (xmin,ymin), (xmax,ymax), (10, 255, 0), 2)

cv2.imshow(frame)
```


### Relevant log output

_No response_