Samuel Alexander / AI Recycle Bin (NDP120) Public
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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

Audible events

Cortex-M7 216MHz

300 ms

340 kB

1024 kB

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mfe-conv2d-e87
PERFORMANCE
LATENCY
300 ms
RAM
340 kB
ROM
1024 kB
Unused
INPUT

1000 ms | 1000 ms

MFE

0.032 | 0.032 | 32

ACCURACY
CLASSIFICATION

0.005 | 100

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

mfcc-conv2d-2ef
PERFORMANCE
LATENCY
300 ms
RAM
340 kB
ROM
1024 kB
Unused
INPUT

1000 ms | 500 ms

MFCC

0.05 | 0.025 | 32

ACCURACY
CLASSIFICATION

0.005 | 100

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

spectr-conv1d-2b1
PERFORMANCE
LATENCY
300 ms
RAM
340 kB
ROM
1024 kB
Unused
INPUT

1000 ms | 1000 ms

SPECTROGRAM

0.075 | 0.075 | -72

ACCURACY
CLASSIFICATION

0.005 | 100

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

mfe-conv1d-eb3
PERFORMANCE
LATENCY
300 ms
RAM
340 kB
ROM
1024 kB
Unused
INPUT

1000 ms | 500 ms

MFE

0.032 | 0.016 | 32

ACCURACY
CLASSIFICATION

0.005 | 100

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

spectr-conv2d-155
PERFORMANCE
LATENCY
300 ms
RAM
340 kB
ROM
1024 kB
Unused
INPUT

1000 ms | 1000 ms

SPECTROGRAM

0.075 | 0.075 | -72

ACCURACY
CLASSIFICATION

0.005 | 100

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

mfe-conv2d-cc1
PERFORMANCE
LATENCY
300 ms
RAM
340 kB
ROM
1024 kB
Unused
INPUT

1000 ms | 250 ms

MFE

0.05 | 0.025 | 32

ACCURACY
CLASSIFICATION

0.005 | 100

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

spectr-conv1d-b9d
PERFORMANCE
LATENCY
300 ms
RAM
340 kB
ROM
1024 kB
Unused
INPUT

1000 ms | 250 ms

SPECTROGRAM

0.075 | 0.0375 | -32

ACCURACY
CLASSIFICATION

0.005 | 100

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

mfe-conv1d-ca7
PERFORMANCE
LATENCY
300 ms
RAM
340 kB
ROM
1024 kB
Unused
INPUT

1000 ms | 1000 ms

MFE

0.05 | 0.05 | 32

ACCURACY
CLASSIFICATION

0.005 | 100

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

mfcc-conv1d-d54
PERFORMANCE
LATENCY
300 ms
RAM
340 kB
ROM
1024 kB
Unused
INPUT

1000 ms | 500 ms

MFCC

0.05 | 0.05 | 40

ACCURACY
CLASSIFICATION

0.005 | 100

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

mfe-conv1d-ecf
PERFORMANCE
LATENCY
300 ms
RAM
340 kB
ROM
1024 kB
Unused
INPUT

1000 ms | 1000 ms

MFE

0.02 | 0.02 | 32

ACCURACY
CLASSIFICATION

0.005 | 100

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

mfcc-conv1d-92c
PERFORMANCE
LATENCY
300 ms
RAM
340 kB
ROM
1024 kB
Unused
INPUT

1000 ms | 1000 ms

MFCC

0.05 | 0.025 | 40

ACCURACY
CLASSIFICATION

0.005 | 100

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

mfcc-conv2d-0ce
PERFORMANCE
LATENCY
300 ms
RAM
340 kB
ROM
1024 kB
Unused
INPUT

1000 ms | 500 ms

MFCC

0.05 | 0.05 | 40

ACCURACY
CLASSIFICATION

0.005 | 100

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

mfcc-conv1d-8da
PERFORMANCE
LATENCY
300 ms
RAM
340 kB
ROM
1024 kB
Unused
INPUT

1000 ms | 1000 ms

MFCC

0.032 | 0.016 | 32

ACCURACY
CLASSIFICATION

0.005 | 100

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

spectr-conv2d-be4
PERFORMANCE
LATENCY
300 ms
RAM
340 kB
ROM
1024 kB
Unused
INPUT

1000 ms | 500 ms

SPECTROGRAM

0.075 | 0.075 | -32

ACCURACY
CLASSIFICATION

0.005 | 100

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

spectr-conv1d-8b8
PERFORMANCE
LATENCY
300 ms
RAM
340 kB
ROM
1024 kB
Unused
INPUT

1000 ms | 500 ms

SPECTROGRAM

0.05 | 0.05 | -32

ACCURACY
CLASSIFICATION

0.005 | 100

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

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