IoT - Development & Test / ShowerTime Public
Target: Cortex-M7 216MHz Clone this project
Experiments EON Tuner
The EON Tuner helps you quickly run hyper-parameter sweeps that explore different pre-processing + model architectures optimized for your defined objectives. Clone this project to use the EON Tuner.

Target

No name set

Cortex-M7 216MHz

100 ms

340 kB

1024 kB

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General

F1-score

Precision

Recall

100%
mfe-conv2d-915
PERFORMANCE
LATENCY
15 ms of 100 ms
RAM
20 kB of 340 kB
ROM
37 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
500 ms |
Enabled

MFE

0.02 | 0.02 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 100%

Type Filters Kernel Rate
Data augmentation
conv2d 8 3 -
conv2d 16 3 -
conv2d 32 3 -
dropout - - 0.25

10/14/2022, 3:07:38 PM

100%
mfe-conv1d-88c
PERFORMANCE
LATENCY
146 ms of 100 ms
Exceeds target by 46 ms
RAM
37 kB of 340 kB
ROM
44 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

2000 ms |
1000 ms |
Enabled

MFE

0.032 | 0.016 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 100%

Type Filters Kernel Rate
conv1d 32 3 -
conv1d 64 3 -
dropout - - 0.25

10/14/2022, 3:10:50 PM

100%
mfe-conv2d-c19
PERFORMANCE
LATENCY
207 ms of 100 ms
Exceeds target by 107 ms
RAM
38 kB of 340 kB
ROM
56 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
500 ms |
Enabled

MFE

0.032 | 0.032 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 100%

Type Filters Kernel Rate
conv2d 16 3 -
conv2d 32 3 -
conv2d 64 3 -
dropout - - 0.25

10/14/2022, 3:11:38 PM

100%
spectr-conv2d-bf1
PERFORMANCE
LATENCY
107 ms of 100 ms
Exceeds target by 7 ms
RAM
77 kB of 340 kB
ROM
41 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

SPECTROGRAM

0.025 | 0.0125 | -72

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 100%

Type Filters Kernel Rate
conv2d 8 3 -
conv2d 16 3 -
dropout - - 0.5

10/14/2022, 3:11:48 PM

100%
mfe-conv1d-ed5
PERFORMANCE
LATENCY
176 ms of 100 ms
Exceeds target by 76 ms
RAM
37 kB of 340 kB
ROM
44 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

2000 ms |
1000 ms |
Enabled

MFE

0.032 | 0.016 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 100%

Type Filters Kernel Rate
conv1d 32 3 -
conv1d 64 3 -
dropout - - 0.5

10/14/2022, 3:12:32 PM

100%
spectr-conv2d-29f
PERFORMANCE
LATENCY
240 ms of 100 ms
Exceeds target by 140 ms
RAM
59 kB of 340 kB
ROM
58 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

2000 ms |
500 ms |
Enabled

SPECTROGRAM

0.075 | 0.0375 | -52

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 100%

Type Filters Kernel Rate
conv2d 8 3 -
conv2d 16 3 -
conv2d 32 3 -
conv2d 64 3 -
dropout - - 0.25

10/14/2022, 3:16:05 PM

100%
spectr-conv1d-3c8
PERFORMANCE
LATENCY
343 ms of 100 ms
Exceeds target by 243 ms
RAM
47 kB of 340 kB
ROM
47 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

4000 ms |
2000 ms |
Enabled

SPECTROGRAM

0.025 | 0.0125 | -52

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 100%

Type Filters Kernel Rate
conv1d 16 3 -
conv1d 32 3 -
conv1d 64 3 -
dropout - - 0.5

10/14/2022, 3:16:37 PM

100%
mfe-conv1d-025
PERFORMANCE
LATENCY
129 ms of 100 ms
Exceeds target by 29 ms
RAM
22 kB of 340 kB
ROM
173 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

4000 ms |
1000 ms |
Enabled

MFE

0.05 | 0.025 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 100%

Type Filters Kernel Rate
Data augmentation
conv1d 32 3 -
conv1d 64 3 -
conv1d 128 3 -
conv1d 256 3 -
dropout - - 0.25

10/14/2022, 3:17:52 PM

100%
mfe-conv1d-bd3
PERFORMANCE
LATENCY
106 ms of 100 ms
Exceeds target by 6 ms
RAM
34 kB of 340 kB
ROM
70 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
500 ms |
Enabled

MFE

0.02 | 0.01 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 100%

Type Filters Kernel Rate
conv1d 32 3 -
conv1d 64 3 -
conv1d 128 3 -
dropout - - 0.25

10/14/2022, 3:19:31 PM

100%
mfe-conv1d-2af
PERFORMANCE
LATENCY
106 ms of 100 ms
Exceeds target by 6 ms
RAM
33 kB of 340 kB
ROM
42 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
250 ms |
Enabled

MFE

0.02 | 0.01 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 100%

Type Filters Kernel Rate
Data augmentation
conv1d 16 3 -
conv1d 32 3 -
conv1d 64 3 -
dropout - - 0.5

10/14/2022, 3:21:03 PM

100%
spectr-conv2d-fc0
PERFORMANCE
LATENCY
28 ms of 100 ms
RAM
49 kB of 340 kB
ROM
41 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

2000 ms |
2000 ms |
Enabled

SPECTROGRAM

0.075 | 0.075 | -52

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 100%

Type Filters Kernel Rate
conv2d 16 3 -
conv2d 32 3 -
dropout - - 0.25

10/14/2022, 3:21:06 PM

100%
mfe-conv2d-a48
PERFORMANCE
LATENCY
68 ms of 100 ms
RAM
42 kB of 340 kB
ROM
54 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.05 | 0.05 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 100%

Type Filters Kernel Rate
conv2d 32 3 -
conv2d 64 3 -
dropout - - 0.25

10/14/2022, 3:22:55 PM

98%
mfe-conv1d-083
PERFORMANCE
LATENCY
105 ms of 100 ms
Exceeds target by 5 ms
RAM
36 kB of 340 kB
ROM
69 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.02 | 0.01 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 98%

Type Filters Kernel Rate
conv1d 16 3 -
conv1d 32 3 -
conv1d 64 3 -
conv1d 128 3 -
dropout - - 0.5

10/14/2022, 3:22:39 PM

96%
mfe-conv2d-7a1
PERFORMANCE
LATENCY
88 ms of 100 ms
RAM
42 kB of 340 kB
ROM
56 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
500 ms |
Enabled

MFE

0.032 | 0.016 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 96%

Type Filters Kernel Rate
Data augmentation
conv2d 8 3 -
conv2d 16 3 -
conv2d 32 3 -
conv2d 64 3 -
dropout - - 0.5

10/14/2022, 3:19:07 PM

93%
mfe-conv1d-d41
PERFORMANCE
LATENCY
106 ms of 100 ms
Exceeds target by 6 ms
RAM
31 kB of 340 kB
ROM
31 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

2000 ms |
1000 ms |
Enabled

MFE

0.02 | 0.02 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 93%

Type Filters Kernel Rate
Data augmentation
conv1d 8 3 -
conv1d 16 3 -
dropout - - 0.25

10/14/2022, 3:07:37 PM

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