Edge Impulse Inc. / Keywords Detection - EON Tuner Search Space Public
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

Cortex-M7 216MHz

100 ms

340 kB

1024 kB

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F1-score

Precision

Recall

92%
mfe-conv2d-b5c
PERFORMANCE
LATENCY
220 ms of 100 ms
Exceeds target by 120 ms
RAM
43 kB of 340 kB
ROM
86 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.032 | 0.016 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 92%

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

9/27/2022, 6:15:49 PM

90%
mfcc-conv1d-ab2
PERFORMANCE
LATENCY
312 ms of 100 ms
Exceeds target by 212 ms
RAM
34 kB of 340 kB
ROM
200 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.02 | 0.02 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 90%

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

9/27/2022, 5:16:54 PM

88%
mfcc-conv2d-9cc
PERFORMANCE
LATENCY
301 ms of 100 ms
Exceeds target by 201 ms
RAM
36 kB of 340 kB
ROM
69 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.02 | 0.02 | 40

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 88%

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

9/27/2022, 5:41:49 PM

88%
mfe-conv1d-8d7
PERFORMANCE
LATENCY
126 ms of 100 ms
Exceeds target by 26 ms
RAM
35 kB of 340 kB
ROM
102 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 | 88%

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

9/27/2022, 5:33:42 PM

88%
mfcc-conv2d-cbb
PERFORMANCE
LATENCY
103 ms of 100 ms
Exceeds target by 3 ms
RAM
28 kB of 340 kB
ROM
66 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.02 | 0.02 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 88%

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

9/27/2022, 5:39:23 PM

88%
mfe-conv1d-1a8
PERFORMANCE
LATENCY
125 ms of 100 ms
Exceeds target by 25 ms
RAM
35 kB of 340 kB
ROM
102 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 | 88%

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

9/27/2022, 5:17:14 PM

88%
mfe-conv2d-9b1
PERFORMANCE
LATENCY
204 ms of 100 ms
Exceeds target by 104 ms
RAM
50 kB of 340 kB
ROM
76 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.02 | 0.02 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 88%

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

9/27/2022, 6:18:02 PM

88%
mfe-mobilenetv1-725
PERFORMANCE
LATENCY
154 ms of 100 ms
Exceeds target by 54 ms
RAM
71 kB of 340 kB
ROM
153 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
500 ms |
Enabled

MFE

0.02 | 0.01 | 40

ACCURACY (KERAS-TRANSFER-KWS)
TRANSFER LEARNING (KEYWORD SPOTTING)

0.01 | 30 | 88%

MobileNetV1 0.1
128 | 0.1

9/27/2022, 5:14:55 PM

88%
mfe-conv1d-f2e
PERFORMANCE
LATENCY
229 ms of 100 ms
Exceeds target by 129 ms
RAM
31 kB of 340 kB
ROM
201 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.02 | 0.02 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 88%

Type Filters Kernel Rate
Data augmentation
conv1d 32 3 -
conv1d 64 3 -
conv1d 128 3 -
conv1d 256 3 -
dropout - - 0.5

9/27/2022, 5:55:34 PM

87%
mfcc-conv1d-a94
PERFORMANCE
LATENCY
190 ms of 100 ms
Exceeds target by 90 ms
RAM
34 kB of 340 kB
ROM
200 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.02 | 0.02 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 87%

Type Filters Kernel Rate
Data augmentation
conv1d 32 3 -
conv1d 64 3 -
conv1d 128 3 -
conv1d 256 3 -
dropout - - 0.5

9/27/2022, 5:30:46 PM

87%
mfe-conv1d-f83
PERFORMANCE
LATENCY
107 ms of 100 ms
Exceeds target by 7 ms
RAM
33 kB of 340 kB
ROM
64 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 | 87%

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

9/27/2022, 6:22:33 PM

87%
mfcc-conv2d-587
PERFORMANCE
LATENCY
201 ms of 100 ms
Exceeds target by 101 ms
RAM
42 kB of 340 kB
ROM
87 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.05 | 0.025 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 87%

Type Filters Kernel Rate
conv2d 32 3 -
conv2d 64 3 -
dropout - - 0.5

9/27/2022, 6:34:45 PM

87%
mfe-conv1d-0ad
PERFORMANCE
LATENCY
127 ms of 100 ms
Exceeds target by 27 ms
RAM
35 kB of 340 kB
ROM
102 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 | 87%

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

9/27/2022, 5:21:28 PM

87%
mfcc-conv1d-edd
PERFORMANCE
LATENCY
251 ms of 100 ms
Exceeds target by 151 ms
RAM
36 kB of 340 kB
ROM
200 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.02 | 0.02 | 40

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 87%

Type Filters Kernel Rate
Data augmentation
conv1d 32 3 -
conv1d 64 3 -
conv1d 128 3 -
conv1d 256 3 -
dropout - - 0.5

9/27/2022, 5:23:14 PM

87%
mfcc-conv2d-d07
PERFORMANCE
LATENCY
174 ms of 100 ms
Exceeds target by 74 ms
RAM
35 kB of 340 kB
ROM
85 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.032 | 0.032 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 87%

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

9/27/2022, 6:25:48 PM