David Schwarz / Ball Bearing Fault Detection Public
Target: TI LAUNCHXL-CC1352P (Cortex-M4F 48MHz) Clone this project

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

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Keyword spotting

TI LAUNCHXL-CC1352P (Cortex-M4F 48MHz)

100 ms

80 kB

352 kB

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Recall

100%
mfe-conv1d-252
Show logs
PERFORMANCE
LATENCY
76 ms of 100 ms
RAM
23 kB of 80 kB
ROM
151 kB of 352 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
250 ms |
Enabled

MFE

0.05 | 0.05 | 40

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

9/21/2021, 7:56:48 PM

99%
mfe-conv1d-eee
Show logs
PERFORMANCE
LATENCY
26 ms of 100 ms
RAM
18 kB of 80 kB
ROM
36 kB of 352 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
250 ms |
Enabled

MFE

0.05 | 0.05 | 40

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

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

9/21/2021, 7:53:23 PM

90%
mfe-conv1d-9c6
Show logs
PERFORMANCE
LATENCY
32 ms of 100 ms
RAM
18 kB of 80 kB
ROM
46 kB of 352 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
500 ms |
Enabled

MFE

0.05 | 0.05 | 40

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

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

9/21/2021, 7:59:22 PM

81%
mfe-conv1d-332
Show logs
PERFORMANCE
LATENCY
68 ms of 100 ms
RAM
18 kB of 80 kB
ROM
44 kB of 352 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
250 ms |
Enabled

MFE

0.05 | 0.05 | 40

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

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

9/21/2021, 7:54:03 PM

77%
mfe-conv1d-2ca
Show logs
PERFORMANCE
LATENCY
42 ms of 100 ms
RAM
19 kB of 80 kB
ROM
44 kB of 352 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
250 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

9/21/2021, 7:55:14 PM

73%
mfe-conv1d-d77
Show logs
PERFORMANCE
LATENCY
42 ms of 100 ms
RAM
19 kB of 80 kB
ROM
44 kB of 352 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
500 ms |
Enabled

MFE

0.05 | 0.05 | 40

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

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

9/21/2021, 7:52:08 PM

71%
mfe-conv1d-5b7
Show logs
PERFORMANCE
LATENCY
84 ms of 100 ms
RAM
26 kB of 80 kB
ROM
231 kB of 352 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
250 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 -
conv1d 128 3 -
dropout - - 0.5
dense 64 - -
dropout - - 0.5

9/21/2021, 7:56:02 PM

70%
mfe-conv1d-dce
Show logs
PERFORMANCE
LATENCY
42 ms of 100 ms
RAM
19 kB of 80 kB
ROM
44 kB of 352 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
conv1d 16 3 -
conv1d 32 3 -
conv1d 64 3 -
dropout - - 0.25

9/21/2021, 7:55:13 PM

69%
mfe-conv1d-55b
Show logs
PERFORMANCE
LATENCY
68 ms of 100 ms
RAM
20 kB of 80 kB
ROM
56 kB of 352 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
250 ms |
Enabled

MFE

0.05 | 0.05 | 40

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

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

9/21/2021, 7:52:43 PM

67%
mfe-conv1d-990
Show logs
PERFORMANCE
LATENCY
84 ms of 100 ms
RAM
20 kB of 80 kB
ROM
71 kB of 352 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
conv1d 16 3 -
conv1d 32 3 -
conv1d 64 3 -
conv1d 128 3 -
dropout - - 0.25
dense 64 - -
dropout - - 0.25

9/21/2021, 7:58:19 PM

63%
mfe-conv1d-bc8
Show logs
PERFORMANCE
LATENCY
126 ms of 100 ms
Exceeds target by 26 ms
RAM
20 kB of 80 kB
ROM
71 kB of 352 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
conv1d 32 3 -
conv1d 64 3 -
conv1d 128 3 -
dropout - - 0.25

9/21/2021, 7:58:05 PM

63%
mfe-conv1d-29e
Show logs
PERFORMANCE
LATENCY
42 ms of 100 ms
RAM
19 kB of 80 kB
ROM
44 kB of 352 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 - - 0.25

9/21/2021, 7:51:22 PM

61%
mfe-conv1d-359
Show logs
PERFORMANCE
LATENCY
76 ms of 100 ms
RAM
24 kB of 80 kB
ROM
71 kB of 352 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
500 ms |
Enabled

MFE

0.05 | 0.05 | 40

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

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

9/21/2021, 7:57:07 PM

61%
mfe-conv1d-8ca
Show logs
PERFORMANCE
LATENCY
126 ms of 100 ms
Exceeds target by 26 ms
RAM
20 kB of 80 kB
ROM
71 kB of 352 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
500 ms |
Enabled

MFE

0.05 | 0.05 | 40

ACCURACY
NEURAL NETWORK (KERAS)

0.005 | 100

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

9/21/2021, 7:58:09 PM

59%
mfe-conv1d-d7f
Show logs
PERFORMANCE
LATENCY
76 ms of 100 ms
RAM
21 kB of 80 kB
ROM
71 kB of 352 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
conv1d 16 3 -
conv1d 32 3 -
conv1d 64 3 -
conv1d 128 3 -
dropout - - 0.5

9/21/2021, 7:52:31 PM

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