Borui Wei / Drum Rack Public
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

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General

F1-score

Precision

Recall

82%
spectr-conv1d-318
PERFORMANCE
LATENCY
135 ms of 100 ms
Exceeds target by 35 ms
RAM
26 kB of 256 kB
ROM
66 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

2000 ms |
2000 ms |
Enabled

SPECTROGRAM

0.05 | 0.05 | -72

ACCURACY (KERAS)
CLASSIFICATION

0.005 | 100 | 82%

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

4/28/2023, 9:29:46 PM

81%
mfe-conv1d-a61
PERFORMANCE
LATENCY
227 ms of 100 ms
Exceeds target by 127 ms
RAM
20 kB of 256 kB
ROM
30 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.032 | 0.016 | 32

ACCURACY (KERAS)
CLASSIFICATION

0.005 | 100 | 81%

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

4/28/2023, 9:26:48 PM

78%
spectr-conv1d-79e
PERFORMANCE
LATENCY
193 ms of 100 ms
Exceeds target by 93 ms
RAM
38 kB of 256 kB
ROM
40 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

SPECTROGRAM

0.025 | 0.0125 | -32

ACCURACY (KERAS)
CLASSIFICATION

0.005 | 100 | 78%

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

4/28/2023, 9:23:55 PM

78%
spectr-conv1d-e94
PERFORMANCE
LATENCY
5939 ms of 100 ms
Exceeds target by 5839 ms
RAM
73 kB of 256 kB
ROM
66 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

SPECTROGRAM

0.025 | 0.025 | -72

ACCURACY (KERAS)
CLASSIFICATION

0.005 | 100 | 78%

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

4/28/2023, 9:27:45 PM

78%
spectr-conv1d-452
PERFORMANCE
LATENCY
193 ms of 100 ms
Exceeds target by 93 ms
RAM
30 kB of 256 kB
ROM
41 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

2000 ms |
2000 ms |
Enabled

SPECTROGRAM

0.075 | 0.0375 | -72

ACCURACY (KERAS)
CLASSIFICATION

0.005 | 100 | 78%

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

4/28/2023, 9:29:50 PM

73%
spectr-conv2d-333
PERFORMANCE
LATENCY
156 ms of 100 ms
Exceeds target by 56 ms
RAM
30 kB of 256 kB
ROM
30 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

SPECTROGRAM

0.075 | 0.0375 | -32

ACCURACY (KERAS)
CLASSIFICATION

0.005 | 100 | 73%

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

4/28/2023, 9:29:13 PM

73%
mfe-conv1d-3a8
PERFORMANCE
LATENCY
162 ms of 100 ms
Exceeds target by 62 ms
RAM
17 kB of 256 kB
ROM
64 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.032 | 0.032 | 32

ACCURACY (KERAS)
CLASSIFICATION

0.005 | 100 | 73%

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

4/28/2023, 9:32:55 PM

72%
spectr-conv1d-a7b
PERFORMANCE
LATENCY
56 ms of 100 ms
RAM
18 kB of 256 kB
ROM
66 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

SPECTROGRAM

0.075 | 0.075 | -32

ACCURACY (KERAS)
CLASSIFICATION

0.005 | 100 | 72%

Type Filters Kernel Rate
conv1d 16 3 -
conv1d 32 3 -
conv1d 64 3 -
conv1d 128 3 -
dropout - - 0.25

4/28/2023, 9:25:56 PM

70%
mfe-conv1d-c9a
PERFORMANCE
LATENCY
242 ms of 100 ms
Exceeds target by 142 ms
RAM
20 kB of 256 kB
ROM
30 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

2000 ms |
2000 ms |
Enabled

MFE

0.032 | 0.032 | 32

ACCURACY (KERAS)
CLASSIFICATION

0.005 | 100 | 70%

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

4/28/2023, 9:22:25 PM

70%
spectr-conv1d-9ad
PERFORMANCE
LATENCY
114 ms of 100 ms
Exceeds target by 14 ms
RAM
22 kB of 256 kB
ROM
40 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

SPECTROGRAM

0.025 | 0.025 | -52

ACCURACY (KERAS)
CLASSIFICATION

0.005 | 100 | 70%

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

4/28/2023, 9:31:22 PM

69%
mfe-conv1d-897
PERFORMANCE
LATENCY
192 ms of 100 ms
Exceeds target by 92 ms
RAM
20 kB of 256 kB
ROM
65 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.05 | 0.025 | 32

ACCURACY (KERAS)
CLASSIFICATION

0.005 | 100 | 69%

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

4/28/2023, 9:27:39 PM

69%
mfe-conv1d-c93
PERFORMANCE
LATENCY
334 ms of 100 ms
Exceeds target by 234 ms
RAM
26 kB of 256 kB
ROM
38 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

2000 ms |
2000 ms |
Enabled

MFE

0.05 | 0.025 | 32

ACCURACY (KERAS)
CLASSIFICATION

0.005 | 100 | 69%

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

4/28/2023, 9:25:30 PM

69%
mfe-conv1d-fc5
PERFORMANCE
LATENCY
330 ms of 100 ms
Exceeds target by 230 ms
RAM
24 kB of 256 kB
ROM
27 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

2000 ms |
2000 ms |
Enabled

MFE

0.05 | 0.025 | 32

ACCURACY (KERAS)
CLASSIFICATION

0.005 | 100 | 69%

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

4/28/2023, 9:26:40 PM

69%
mfe-conv1d-48d
PERFORMANCE
LATENCY
230 ms of 100 ms
Exceeds target by 130 ms
RAM
20 kB of 256 kB
ROM
30 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.032 | 0.016 | 32

ACCURACY (KERAS)
CLASSIFICATION

0.005 | 100 | 69%

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

4/28/2023, 9:30:54 PM

67%
spectr-conv1d-1f4
PERFORMANCE
LATENCY
131 ms of 100 ms
Exceeds target by 31 ms
RAM
26 kB of 256 kB
ROM
66 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

SPECTROGRAM

0.05 | 0.025 | -72

ACCURACY (KERAS)
CLASSIFICATION

0.005 | 100 | 67%

Type Filters Kernel Rate
conv1d 16 3 -
conv1d 32 3 -
conv1d 64 3 -
conv1d 128 3 -
dropout - - 0.25

4/28/2023, 9:26:00 PM