Edge Impulse Inc. / Tutorial: Responding to your voice Public
Target: Arduino Nano 33 BLE Sense (Cortex-M4F 64MHz) 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

Arduino Nano 33 BLE Sense (Cortex-M4F 64MHz)

100 ms

256 kB

1024 kB

Filters

Status

DSP type

Model type

View

Data set

Variant

Sort

General

F1-score

Precision

Recall

94%
mfe-conv2d-49f
PERFORMANCE
LATENCY
3082 ms of 100 ms
Exceeds target by 2982 ms
RAM
42 kB of 256 kB
ROM
57 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 | 94%

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

10/27/2022, 5:28:01 PM

93%
mfcc-conv2d-548
PERFORMANCE
LATENCY
2293 ms of 100 ms
Exceeds target by 2193 ms
RAM
16 kB of 256 kB
ROM
55 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.05 | 0.025 | 40

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 93%

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

10/27/2022, 5:43:13 PM

91%
mfe-conv2d-1b2
PERFORMANCE
LATENCY
245 ms of 100 ms
Exceeds target by 145 ms
RAM
36 kB of 256 kB
ROM
40 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.032 | 0.032 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 91%

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

10/27/2022, 5:19:47 PM

90%
mfe-conv2d-ad1
PERFORMANCE
LATENCY
3842 ms of 100 ms
Exceeds target by 3742 ms
RAM
18 kB of 256 kB
ROM
56 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 | 90%

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

10/27/2022, 5:40:24 PM

90%
mfe-conv2d-547
PERFORMANCE
LATENCY
4841 ms of 100 ms
Exceeds target by 4741 ms
RAM
35 kB of 256 kB
ROM
44 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 | 90%

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

10/27/2022, 5:51:05 PM

89%
mfcc-conv2d-3d0
PERFORMANCE
LATENCY
5281 ms of 100 ms
Exceeds target by 5181 ms
RAM
35 kB of 256 kB
ROM
55 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.05 | 0.025 | 40

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 89%

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

10/27/2022, 5:30:15 PM

89%
mfe-conv2d-e13
PERFORMANCE
LATENCY
4649 ms of 100 ms
Exceeds target by 4549 ms
RAM
31 kB of 256 kB
ROM
56 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 | 89%

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

10/27/2022, 5:35:36 PM

89%
mfcc-conv2d-1c3
PERFORMANCE
LATENCY
2731 ms of 100 ms
Exceeds target by 2631 ms
RAM
20 kB of 256 kB
ROM
56 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.02 | 0.01 | 40

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 89%

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

10/27/2022, 5:47:16 PM

89%
mfcc-conv2d-798
PERFORMANCE
LATENCY
4718 ms of 100 ms
Exceeds target by 4618 ms
RAM
16 kB of 256 kB
ROM
55 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 | 89%

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

10/27/2022, 5:15:32 PM

89%
mfcc-conv2d-e94
PERFORMANCE
LATENCY
1543 ms of 100 ms
Exceeds target by 1443 ms
RAM
33 kB of 256 kB
ROM
55 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 | 89%

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

10/27/2022, 5:42:23 PM

89%
mfcc-conv2d-ebb
PERFORMANCE
LATENCY
2892 ms of 100 ms
Exceeds target by 2792 ms
RAM
36 kB of 256 kB
ROM
129 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 | 89%

Type Filters Kernel Rate
conv2d 16 3 -
conv2d 32 3 -
conv2d 64 3 -
conv2d 128 3 -
dropout - - 0.5

10/27/2022, 5:46:10 PM

89%
mfe-conv2d-4ac
PERFORMANCE
LATENCY
4192 ms of 100 ms
Exceeds target by 4092 ms
RAM
23 kB of 256 kB
ROM
40 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.032 | 0.032 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 89%

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

10/27/2022, 5:48:32 PM

88%
mfcc-conv2d-404
PERFORMANCE
LATENCY
15012 ms of 100 ms
Exceeds target by 14912 ms
RAM
32 kB of 256 kB
ROM
129 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 | 88%

Type Filters Kernel Rate
Data augmentation
conv2d 16 3 -
conv2d 32 3 -
conv2d 64 3 -
conv2d 128 3 -
dropout - - 0.25

10/27/2022, 5:26:27 PM

87%
mfcc-conv2d-951
PERFORMANCE
LATENCY
7720 ms of 100 ms
Exceeds target by 7620 ms
RAM
45 kB of 256 kB
ROM
56 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.02 | 0.01 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 87%

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

10/27/2022, 5:26:51 PM

87%
mfe-conv2d-170
PERFORMANCE
LATENCY
1348 ms of 100 ms
Exceeds target by 1248 ms
RAM
49 kB of 256 kB
ROM
44 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 | 87%

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

10/27/2022, 5:34:23 PM

Help & documentation Get help on the forum Send feedback Contact sales
Join us
Terms of Service Privacy policy