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
MacBook Pro 16" 2020 (Intel Core i9 2.4GHz)
100 ms
264 kB
2048 kB
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F1-score
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Recall
raw-dense-278
PERFORMANCE
LATENCY
1 ms of 100 ms
RAM
8 kB of 264 kB
ROM
71 kB of 2048 kB
DSP NN Unused
TIME-SERIES INPUT
30 ms |
30 ms |
Enabled
RAW
1
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.0005 | 30 | 0%
Type | Filters | Kernel | Rate |
---|---|---|---|
dense | 40 | - | - |
dense | 20 | - | - |
dense | 10 | - | - |
dropout | - | - | 0.5 |
10/6/2022, 4:38:35 PM
raw-dense-e18
PERFORMANCE
LATENCY
1 ms of 100 ms
RAM
7 kB of 264 kB
ROM
67 kB of 2048 kB
DSP NN Unused
TIME-SERIES INPUT
30 ms |
30 ms |
Enabled
RAW
1
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.0000105 | 999 | 0%
Type | Filters | Kernel | Rate |
---|---|---|---|
dense | 33 | - | - |
dense | 25 | - | - |
10/6/2022, 4:39:55 PM
raw-dense-8f8
PERFORMANCE
LATENCY
1 ms of 100 ms
RAM
8 kB of 264 kB
ROM
59 kB of 2048 kB
DSP NN Unused
TIME-SERIES INPUT
30 ms |
30 ms |
Enabled
RAW
1
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.0005 | 30 | 0%
Type | Filters | Kernel | Rate |
---|---|---|---|
dense | 20 | - | - |
dense | 10 | - | - |
dense | 5 | - | - |
dropout | - | - | 0.5 |
10/6/2022, 4:40:22 PM
25%
raw-conv1d-42e
PERFORMANCE
LATENCY
31 ms of 100 ms
RAM
47 kB of 264 kB
ROM
94 kB of 2048 kB
DSP NN Unused
TIME-SERIES INPUT
30 ms |
30 ms |
Enabled
RAW
1
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.0005 | 30 | 25%
Type | Filters | Kernel | Rate |
---|---|---|---|
conv1d | 32 | 3 | - |
conv1d | 64 | 3 | - |
dropout | - | - | 0.25 |
10/6/2022, 4:42:10 PM
raw-conv2d-a2e
PERFORMANCE
LATENCY
5 ms of 100 ms
RAM
19 kB of 264 kB
ROM
91 kB of 2048 kB
DSP NN Unused
TIME-SERIES INPUT
30 ms |
30 ms |
Enabled
RAW
1
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.0005 | 30 | 0%
Type | Filters | Kernel | Rate |
---|---|---|---|
conv2d | 8 | 3 | - |
conv2d | 16 | 3 | - |
conv2d | 32 | 3 | - |
conv2d | 64 | 3 | - |
dropout | - | - | 0.5 |
10/6/2022, 4:43:02 PM
raw-dense-106
PERFORMANCE
LATENCY
1 ms of 100 ms
RAM
8 kB of 264 kB
ROM
71 kB of 2048 kB
DSP NN Unused
TIME-SERIES INPUT
30 ms |
30 ms |
Enabled
RAW
1
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.0005 | 30 | 0%
Type | Filters | Kernel | Rate |
---|---|---|---|
dense | 40 | - | - |
dense | 20 | - | - |
dense | 10 | - | - |
dropout | - | - | 0.25 |
10/6/2022, 4:44:25 PM
raw-conv2d-273
PERFORMANCE
LATENCY
10 ms of 100 ms
RAM
15 kB of 264 kB
ROM
66 kB of 2048 kB
DSP NN Unused
TIME-SERIES INPUT
30 ms |
30 ms |
Enabled
RAW
1
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.0005 | 30 | 0%
Type | Filters | Kernel | Rate |
---|---|---|---|
conv2d | 8 | 3 | - |
conv2d | 16 | 3 | - |
dropout | - | - | 0.25 |
10/6/2022, 4:46:08 PM
raw-dense-405
PERFORMANCE
LATENCY
1 ms of 100 ms
RAM
7 kB of 264 kB
ROM
71 kB of 2048 kB
DSP NN Unused
TIME-SERIES INPUT
30 ms |
30 ms |
Enabled
RAW
1
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.0005 | 30 | 0%
Type | Filters | Kernel | Rate |
---|---|---|---|
dense | 40 | - | - |
dense | 20 | - | - |
dropout | - | - | 0.25 |
10/6/2022, 4:46:13 PM
raw-conv1d-2fb
PERFORMANCE
LATENCY
25 ms of 100 ms
RAM
28 kB of 264 kB
ROM
75 kB of 2048 kB
DSP NN Unused
TIME-SERIES INPUT
30 ms |
30 ms |
Enabled
RAW
1
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.0005 | 30 | 0%
Type | Filters | Kernel | Rate |
---|---|---|---|
conv1d | 16 | 3 | - |
conv1d | 32 | 3 | - |
dropout | - | - | 0.5 |
10/6/2022, 4:48:33 PM
raw-dense-314
PERFORMANCE
LATENCY
1 ms of 100 ms
RAM
8 kB of 264 kB
ROM
59 kB of 2048 kB
DSP NN Unused
TIME-SERIES INPUT
30 ms |
30 ms |
Enabled
RAW
1
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.0005 | 30 | 0%
Type | Filters | Kernel | Rate |
---|---|---|---|
dense | 20 | - | - |
dense | 10 | - | - |
dense | 5 | - | - |
dropout | - | - | 0.25 |
10/6/2022, 4:48:33 PM
raw-conv1d-854
PERFORMANCE
LATENCY
2 ms of 100 ms
RAM
19 kB of 264 kB
ROM
69 kB of 2048 kB
DSP NN Unused
TIME-SERIES INPUT
30 ms |
30 ms |
Enabled
RAW
1
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.0005 | 30 | 0%
Type | Filters | Kernel | Rate |
---|---|---|---|
conv1d | 8 | 3 | - |
conv1d | 16 | 3 | - |
conv1d | 32 | 3 | - |
dropout | - | - | 0.5 |
10/6/2022, 4:50:42 PM
raw-conv1d-1be
PERFORMANCE
LATENCY
17 ms of 100 ms
RAM
32 kB of 264 kB
ROM
110 kB of 2048 kB
DSP NN Unused
TIME-SERIES INPUT
30 ms |
30 ms |
Enabled
RAW
1
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.0005 | 30 | 0%
Type | Filters | Kernel | Rate |
---|---|---|---|
conv1d | 16 | 3 | - |
conv1d | 32 | 3 | - |
conv1d | 64 | 3 | - |
conv1d | 128 | 3 | - |
dropout | - | - | 0.5 |
10/6/2022, 4:51:41 PM
raw-conv1d-d1f
PERFORMANCE
LATENCY
26 ms of 100 ms
RAM
30 kB of 264 kB
ROM
82 kB of 2048 kB
DSP NN Unused
TIME-SERIES INPUT
30 ms |
30 ms |
Enabled
RAW
1
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.0005 | 30 | 0%
Type | Filters | Kernel | Rate |
---|---|---|---|
conv1d | 16 | 3 | - |
conv1d | 32 | 3 | - |
conv1d | 64 | 3 | - |
dropout | - | - | 0.5 |
10/6/2022, 4:53:52 PM
raw-conv1d-6c4
PERFORMANCE
LATENCY
2 ms of 100 ms
RAM
18 kB of 264 kB
ROM
66 kB of 2048 kB
DSP NN Unused
TIME-SERIES INPUT
30 ms |
30 ms |
Enabled
RAW
1
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.0005 | 30 | 0%
Type | Filters | Kernel | Rate |
---|---|---|---|
conv1d | 8 | 3 | - |
conv1d | 16 | 3 | - |
dropout | - | - | 0.5 |
10/6/2022, 4:54:42 PM
raw-conv1d-e93
PERFORMANCE
LATENCY
4 ms of 100 ms
RAM
19 kB of 264 kB
ROM
69 kB of 2048 kB
DSP NN Unused
TIME-SERIES INPUT
30 ms |
30 ms |
Enabled
RAW
1
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.0005 | 30 | 0%
Type | Filters | Kernel | Rate |
---|---|---|---|
conv1d | 8 | 3 | - |
conv1d | 16 | 3 | - |
conv1d | 32 | 3 | - |
dropout | - | - | 0.25 |
10/6/2022, 4:56:52 PM
Training output
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