EON Tuner
The EON Tuner helps you find the most optimal architecture for your embedded machine-learning application. Clone this project to use the EON Tuner.
Target
No name set
Vision
Sony Spresense (Cortex-M4F 156MHz)
500 ms
1536 kB
8192 kB
Filters
Status
DSP type
Network type
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Data set
Precision
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General
F1-score
Precision
Recall
PERFORMANCE
LATENCY
615 ms of
500 ms
Exceeds target by 115 ms
RAM
50 kB of
1536 kB
ROM
64 kB of
8192 kB
DSP
NN
Unused
INPUT
64 | 64
IMAGE
Grayscale
ACCURACY
CLASSIFICATION (KERAS)
0.0005 | 10
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| conv2d | 8 | 3 | - |
| conv2d | 16 | 3 | - |
| conv2d | 32 | 3 | - |
| conv2d | 64 | 3 | - |
| dropout | - | - | 0.5 |
7/6/2022, 12:54:06 PM
PERFORMANCE
LATENCY
6216 ms of
500 ms
Exceeds target by 5716 ms
RAM
52 kB of
1536 kB
ROM
50 kB of
8192 kB
DSP
NN
Unused
INPUT
64 | 64
IMAGE
RGB
ACCURACY
CLASSIFICATION (KERAS)
0.0005 | 10
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| conv2d | 8 | 3 | - |
| conv2d | 16 | 3 | - |
| conv2d | 32 | 3 | - |
| dropout | - | - | 0.25 |
7/6/2022, 12:52:48 PM
Training output
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Click 'Run EON Tuner' to begin
