Edge Impulse Inc. / Tutorial: Recognize sounds from audio Public

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

Keyword spotting

Nordic nRF9160 DK (Cortex-M33 64MHz)

100 ms

256 kB

1024 kB

Filters

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DSP type

Network type

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Data set

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General

F1-score

Precision

Recall

100%
mfe-conv1d-270
PERFORMANCE
LATENCY
30 ms of 100 ms
RAM
20 kB of 256 kB
ROM
56 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
500 ms |
Enabled

MFE

0.032 | 0.032 | 40

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

Type Filters Kernel Rate
conv1d 8 3 -
conv1d 16 3 -
conv1d 32 3 -
dropout - - 0.5
dense 64 - -
dropout - - 0.5

12/17/2021, 10:24:17 PM

100%
spectr-conv1d-ecb
PERFORMANCE
LATENCY
119 ms of 100 ms
Exceeds target by 19 ms
RAM
21 kB of 256 kB
ROM
47 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

SPECTROGRAM

0.075 | 0.0375 | -52

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

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

12/17/2021, 10:24:58 PM

100%
spectr-conv1d-387
PERFORMANCE
LATENCY
41 ms of 100 ms
RAM
23 kB of 256 kB
ROM
46 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

2000 ms |
1000 ms |
Enabled

SPECTROGRAM

0.075 | 0.075 | -72

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

Type Filters Kernel Rate
conv1d 8 3 -
conv1d 16 3 -
conv1d 32 3 -
conv1d 64 3 -
dropout - - 0.5

12/17/2021, 10:22:11 PM

100%
mfe-conv1d-de7
PERFORMANCE
LATENCY
28 ms of 100 ms
RAM
24 kB of 256 kB
ROM
43 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.05 | 0.025 | 40

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

Type Filters Kernel Rate
conv1d 8 3 -
conv1d 16 3 -
dropout - - 0.5
dense 64 - -
dropout - - 0.5

12/17/2021, 10:25:59 PM

100%
mfe-conv1d-69a
PERFORMANCE
LATENCY
16 ms of 100 ms
RAM
21 kB of 256 kB
ROM
37 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.05 | 0.05 | 40

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

Type Filters Kernel Rate
Data augmentation
conv1d 8 3 -
conv1d 16 3 -
conv1d 32 3 -
dropout - - 0.25

12/17/2021, 10:27:45 PM

100%
spectr-conv1d-edb
PERFORMANCE
LATENCY
59 ms of 100 ms
RAM
27 kB of 256 kB
ROM
46 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

2000 ms |
2000 ms |
Enabled

SPECTROGRAM

0.05 | 0.05 | -72

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

Type Filters Kernel Rate
conv1d 8 3 -
conv1d 16 3 -
conv1d 32 3 -
conv1d 64 3 -
dropout - - 0.5

12/17/2021, 10:27:54 PM

100%
mfe-conv1d-cc5
PERFORMANCE
LATENCY
116 ms of 100 ms
Exceeds target by 16 ms
RAM
26 kB of 256 kB
ROM
72 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.032 | 0.032 | 40

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

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

12/17/2021, 10:29:04 PM

100%
spectr-conv1d-72e
PERFORMANCE
LATENCY
109 ms of 100 ms
Exceeds target by 9 ms
RAM
31 kB of 256 kB
ROM
77 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

SPECTROGRAM

0.025 | 0.025 | -52

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

Type Filters Kernel Rate
conv1d 16 3 -
conv1d 32 3 -
conv1d 64 3 -
dropout - - 0.25

12/17/2021, 10:29:19 PM

100%
mfe-conv1d-697
PERFORMANCE
LATENCY
49 ms of 100 ms
RAM
24 kB of 256 kB
ROM
46 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.05 | 0.05 | 40

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

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

12/17/2021, 10:29:37 PM

100%
spectr-conv1d-bff
PERFORMANCE
LATENCY
72 ms of 100 ms
RAM
22 kB of 256 kB
ROM
46 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

SPECTROGRAM

0.075 | 0.0375 | -52

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

Type Filters Kernel Rate
Data augmentation
conv1d 16 3 -
conv1d 32 3 -
conv1d 64 3 -
dropout - - 0.25

12/17/2021, 10:29:43 PM

100%
mfe-conv2d-421
PERFORMANCE
LATENCY
170 ms of 100 ms
Exceeds target by 70 ms
RAM
21 kB of 256 kB
ROM
66 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.05 | 0.05 | 40

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

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

12/17/2021, 10:31:08 PM

100%
spectr-conv1d-6e5
PERFORMANCE
LATENCY
21 ms of 100 ms
RAM
17 kB of 256 kB
ROM
41 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

SPECTROGRAM

0.05 | 0.05 | -72

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

Type Filters Kernel Rate
Data augmentation
conv1d 8 3 -
conv1d 16 3 -
dropout - - 0.25
dense 64 - -
dropout - - 0.25

12/17/2021, 10:31:23 PM

100%
mfe-conv1d-dfc
PERFORMANCE
LATENCY
31 ms of 100 ms
RAM
26 kB of 256 kB
ROM
46 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
500 ms |
Enabled

MFE

0.05 | 0.025 | 40

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

Type Filters Kernel Rate
conv1d 8 3 -
conv1d 16 3 -
conv1d 32 3 -
dropout - - 0.25

12/17/2021, 10:32:28 PM

100%
spectr-conv1d-23d
PERFORMANCE
LATENCY
33 ms of 100 ms
RAM
22 kB of 256 kB
ROM
46 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

2000 ms |
2000 ms |
Enabled

SPECTROGRAM

0.075 | 0.075 | -72

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

Type Filters Kernel Rate
conv1d 8 3 -
conv1d 16 3 -
conv1d 32 3 -
dropout - - 0.25
dense 64 - -
dropout - - 0.25

12/17/2021, 10:32:31 PM

100%
spectr-conv1d-1de
PERFORMANCE
LATENCY
100 ms of 100 ms
RAM
21 kB of 256 kB
ROM
90 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
500 ms |
Enabled

SPECTROGRAM

0.05 | 0.05 | -52

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

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

12/17/2021, 10:34:59 PM