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
Raspberry Pi RP2040 (Cortex-M0+ 133MHz)
100 ms
264 kB
2048 kB
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F1-score
Precision
Recall
spectr-conv1d-abf
PERFORMANCE
LATENCY
100 ms
RAM
264 kB
ROM
2048 kB
Unused
TIME-SERIES INPUT
2000 ms |
1000 ms |
Enabled
SPECTROGRAM
0.075 | 0.075 | -52
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.005 | 100
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| Data augmentation | |||
| conv1d | 16 | 3 | - |
| conv1d | 32 | 3 | - |
| conv1d | 64 | 3 | - |
| dropout | - | - | 0.5 |
spectr-conv1d-5d3
PERFORMANCE
LATENCY
100 ms
RAM
264 kB
ROM
2048 kB
Unused
TIME-SERIES INPUT
2000 ms |
1000 ms |
Enabled
SPECTROGRAM
0.05 | 0.05 | -32
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.005 | 100
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| conv1d | 8 | 3 | - |
| conv1d | 16 | 3 | - |
| conv1d | 32 | 3 | - |
| conv1d | 64 | 3 | - |
| dropout | - | - | 0.5 |
spectr-conv1d-187
PERFORMANCE
LATENCY
100 ms
RAM
264 kB
ROM
2048 kB
Unused
TIME-SERIES INPUT
2000 ms |
1000 ms |
Enabled
SPECTROGRAM
0.075 | 0.0375 | -52
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.005 | 100
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| Data augmentation | |||
| conv1d | 16 | 3 | - |
| conv1d | 32 | 3 | - |
| dropout | - | - | 0.5 |
spectr-conv1d-d23
PERFORMANCE
LATENCY
100 ms
RAM
264 kB
ROM
2048 kB
Unused
TIME-SERIES INPUT
2000 ms |
2000 ms |
Enabled
SPECTROGRAM
0.075 | 0.075 | -52
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.005 | 100
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| conv1d | 16 | 3 | - |
| conv1d | 32 | 3 | - |
| conv1d | 64 | 3 | - |
| dropout | - | - | 0.5 |
spectr-conv1d-879
PERFORMANCE
LATENCY
100 ms
RAM
264 kB
ROM
2048 kB
Unused
TIME-SERIES INPUT
2000 ms |
1000 ms |
Enabled
SPECTROGRAM
0.05 | 0.05 | -32
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.005 | 100
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| Data augmentation | |||
| conv1d | 16 | 3 | - |
| conv1d | 32 | 3 | - |
| dropout | - | - | 0.25 |
spectr-conv1d-ec9
PERFORMANCE
LATENCY
100 ms
RAM
264 kB
ROM
2048 kB
Unused
TIME-SERIES INPUT
2000 ms |
2000 ms |
Enabled
SPECTROGRAM
0.05 | 0.05 | -32
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.005 | 100
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| conv1d | 8 | 3 | - |
| conv1d | 16 | 3 | - |
| conv1d | 32 | 3 | - |
| dropout | - | - | 0.25 |
spectr-conv1d-3a6
PERFORMANCE
LATENCY
100 ms
RAM
264 kB
ROM
2048 kB
Unused
TIME-SERIES INPUT
2000 ms |
1000 ms |
Enabled
SPECTROGRAM
0.075 | 0.0375 | -32
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.005 | 100
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| Data augmentation | |||
| conv1d | 16 | 3 | - |
| conv1d | 32 | 3 | - |
| dropout | - | - | 0.25 |
spectr-conv1d-7de
PERFORMANCE
LATENCY
100 ms
RAM
264 kB
ROM
2048 kB
Unused
TIME-SERIES INPUT
2000 ms |
2000 ms |
Enabled
SPECTROGRAM
0.075 | 0.0375 | -72
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.005 | 100
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| Data augmentation | |||
| conv1d | 8 | 3 | - |
| conv1d | 16 | 3 | - |
| conv1d | 32 | 3 | - |
| dropout | - | - | 0.25 |
spectr-conv1d-f73
PERFORMANCE
LATENCY
100 ms
RAM
264 kB
ROM
2048 kB
Unused
TIME-SERIES INPUT
2000 ms |
2000 ms |
Enabled
SPECTROGRAM
0.075 | 0.0375 | -72
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.005 | 100
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| Data augmentation | |||
| conv1d | 16 | 3 | - |
| conv1d | 32 | 3 | - |
| dropout | - | - | 0.25 |
spectr-conv1d-aee
PERFORMANCE
LATENCY
100 ms
RAM
264 kB
ROM
2048 kB
Unused
TIME-SERIES INPUT
2000 ms |
2000 ms |
Enabled
SPECTROGRAM
0.075 | 0.075 | -52
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.005 | 100
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| conv1d | 16 | 3 | - |
| conv1d | 32 | 3 | - |
| conv1d | 64 | 3 | - |
| dropout | - | - | 0.25 |
spectr-conv1d-5ab
PERFORMANCE
LATENCY
100 ms
RAM
264 kB
ROM
2048 kB
Unused
TIME-SERIES INPUT
2000 ms |
1000 ms |
Enabled
SPECTROGRAM
0.05 | 0.05 | -72
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.005 | 100
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| Data augmentation | |||
| conv1d | 8 | 3 | - |
| conv1d | 16 | 3 | - |
| conv1d | 32 | 3 | - |
| dropout | - | - | 0.5 |
spectr-conv1d-5c0
PERFORMANCE
LATENCY
100 ms
RAM
264 kB
ROM
2048 kB
Unused
TIME-SERIES INPUT
2000 ms |
2000 ms |
Enabled
SPECTROGRAM
0.05 | 0.05 | -32
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.005 | 100
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| Data augmentation | |||
| conv1d | 8 | 3 | - |
| conv1d | 16 | 3 | - |
| conv1d | 32 | 3 | - |
| dropout | - | - | 0.25 |
spectr-conv1d-966
PERFORMANCE
LATENCY
100 ms
RAM
264 kB
ROM
2048 kB
Unused
TIME-SERIES INPUT
2000 ms |
1000 ms |
Enabled
SPECTROGRAM
0.05 | 0.05 | -52
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.005 | 100
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| Data augmentation | |||
| conv1d | 16 | 3 | - |
| conv1d | 32 | 3 | - |
| dropout | - | - | 0.25 |
spectr-conv1d-9ba
PERFORMANCE
LATENCY
100 ms
RAM
264 kB
ROM
2048 kB
Unused
TIME-SERIES INPUT
2000 ms |
2000 ms |
Enabled
SPECTROGRAM
0.075 | 0.0375 | -72
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.005 | 100
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| conv1d | 16 | 3 | - |
| conv1d | 32 | 3 | - |
| dropout | - | - | 0.5 |
Training output
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