Mithun / sony-analog-meter Public
Target: Sony Spresense (Cortex-M4F 156MHz) Clone this project
Experiments 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

Sony Spresense (Cortex-M4F 156MHz)

100 ms

1536 kB

8096 kB

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F1-score

Precision

Recall

100%
rgb-mobilenetv2-5e9
Show logs
PERFORMANCE
LATENCY
2260 ms of 100 ms
Exceeds target by 2160 ms
RAM
738 kB of 1536 kB
ROM
653 kB of 8096 kB
DSP NN Unused
IMAGE INPUT

160 |
160

IMAGE

RGB

ACCURACY (KERAS-TRANSFER-IMAGE)
TRANSFER LEARNING (IMAGES)

0.0005 | 20 | 100%

MobileNetV2 0.35
64 | 0.1

7/6/2022, 12:56:18 PM

100%
rgb-mobilenetv2-5ad
Show logs
PERFORMANCE
LATENCY
2418 ms of 100 ms
Exceeds target by 2318 ms
RAM
738 kB of 1536 kB
ROM
587 kB of 8096 kB
DSP NN Unused
IMAGE INPUT

160 |
160

IMAGE

RGB

ACCURACY (KERAS-TRANSFER-IMAGE)
TRANSFER LEARNING (IMAGES)

0.0005 | 20 | 100%

MobileNetV2 0.35
16 | 0.1 |

7/6/2022, 12:58:56 PM

100%
rgb-mobilenetv2-e4c
Show logs
PERFORMANCE
LATENCY
15714 ms of 100 ms
Exceeds target by 15614 ms
RAM
738 kB of 1536 kB
ROM
587 kB of 8096 kB
DSP NN Unused
IMAGE INPUT

160 |
160

IMAGE

RGB

ACCURACY (KERAS-TRANSFER-IMAGE)
TRANSFER LEARNING (IMAGES)

0.0005 | 20 | 100%

MobileNetV2 160x160 0.35
16 | 0.5

7/6/2022, 1:00:17 PM

100%
rgb-mobilenetv2-0e8
Show logs
PERFORMANCE
LATENCY
962 ms of 100 ms
Exceeds target by 862 ms
RAM
674 kB of 1536 kB
ROM
241 kB of 8096 kB
DSP NN Unused
IMAGE INPUT

160 |
160

IMAGE

RGB

ACCURACY (KERAS-TRANSFER-IMAGE)
TRANSFER LEARNING (IMAGES)

0.0005 | 20 | 100%

MobileNetV2 0.05
64 | 0.5 |

7/6/2022, 1:00:23 PM

100%
rgb-mobilenetv2-694
Show logs
PERFORMANCE
LATENCY
5763 ms of 100 ms
Exceeds target by 5663 ms
RAM
674 kB of 1536 kB
ROM
175 kB of 8096 kB
DSP NN Unused
IMAGE INPUT

160 |
160

IMAGE

RGB

ACCURACY (KERAS-TRANSFER-IMAGE)
TRANSFER LEARNING (IMAGES)

0.0005 | 20 | 100%

MobileNetV2 0.05
16 | 0.1

7/6/2022, 1:04:09 PM

50%
rgb-mobilenetv1-fd3
Show logs
PERFORMANCE
LATENCY
1082 ms of 100 ms
Exceeds target by 982 ms
RAM
264 kB of 1536 kB
ROM
325 kB of 8096 kB
DSP NN Unused
IMAGE INPUT

160 |
160

IMAGE

RGB

ACCURACY (KERAS-TRANSFER-IMAGE)
TRANSFER LEARNING (IMAGES)

0.0005 | 20 | 50%

MobileNetV1 0.25
64 | 0.1 |

7/6/2022, 1:03:27 PM

rgb-mobilenetv1-372
Show logs
PERFORMANCE
LATENCY
912 ms of 100 ms
Exceeds target by 812 ms
RAM
207 kB of 1536 kB
ROM
229 kB of 8096 kB
DSP NN Unused
IMAGE INPUT

160 |
160

IMAGE

RGB

ACCURACY (KERAS-TRANSFER-IMAGE)
TRANSFER LEARNING (IMAGES)

0.0005 | 20 | 0%

MobileNetV1 0.2
16 | 0.5 |

7/6/2022, 12:54:15 PM

rgb-conv2d-812
Show logs
PERFORMANCE
LATENCY
6216 ms of 100 ms
Exceeds target by 6116 ms
RAM
52 kB of 1536 kB
ROM
50 kB of 8096 kB
DSP NN Unused
IMAGE INPUT

64 |
64

IMAGE

RGB

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.0005 | 10 | 0%

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

7/6/2022, 12:52:48 PM

grayscale-conv2d-7ab
Show logs
PERFORMANCE
LATENCY
615 ms of 100 ms
Exceeds target by 515 ms
RAM
50 kB of 1536 kB
ROM
64 kB of 8096 kB
DSP NN Unused
IMAGE INPUT

64 |
64

IMAGE

Grayscale

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.0005 | 10 | 0%

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

7/6/2022, 12:54:06 PM

grayscale-mobilenetv1-f4c
Show logs
PERFORMANCE
LATENCY
1181 ms of 100 ms
Exceeds target by 1081 ms
RAM
264 kB of 1536 kB
ROM
312 kB of 8096 kB
DSP NN Unused
IMAGE INPUT

160 |
160

IMAGE

Grayscale

ACCURACY (KERAS-TRANSFER-IMAGE)
TRANSFER LEARNING (IMAGES)

0.0005 | 20 | 0%

MobileNetV1 0.25
16 | 0.5

7/6/2022, 1:03:04 PM

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