Developer Relations / Microscope - VeLO Public
Target: Cortex-M4F 80MHz 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

Cortex-M4F 80MHz

100 ms

128 kB

1024 kB

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F1-score

Precision

Recall

grayscale-conv2d-322
PERFORMANCE
LATENCY
111 ms of 100 ms
Exceeds target by 11 ms
RAM
19 kB of 128 kB
ROM
52 kB of 1024 kB
DSP NN Unused
IMAGE INPUT

32 |
32

IMAGE

Grayscale

ACCURACY (KERAS)
CLASSIFICATION

0.0005 | 10 | 0%

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

4/18/2023, 1:24:57 PM

22%
grayscale-mobilenetv1-5a0
PERFORMANCE
LATENCY
224 ms of 100 ms
Exceeds target by 124 ms
RAM
58 kB of 128 kB
ROM
114 kB of 1024 kB
DSP NN Unused
IMAGE INPUT

96 |
96

IMAGE

Grayscale

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

0.0005 | 20 | 22%

MobileNetV1 0.1
64 | 0.5 |

4/18/2023, 1:25:51 PM

rgb-mobilenetv1-5a6
PERFORMANCE
LATENCY
243 ms of 100 ms
Exceeds target by 143 ms
RAM
64 kB of 128 kB
ROM
109 kB of 1024 kB
DSP NN Unused
IMAGE INPUT

96 |
96

IMAGE

RGB

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

0.0005 | 20 | 0%

MobileNetV1 0.1
16 | 0.5

4/18/2023, 1:26:03 PM

rgb-conv2d-298
PERFORMANCE
LATENCY
99 ms of 100 ms
RAM
18 kB of 128 kB
ROM
34 kB of 1024 kB
DSP NN Unused
IMAGE INPUT

32 |
32

IMAGE

RGB

ACCURACY (KERAS)
CLASSIFICATION

0.0005 | 10 | 0%

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

4/18/2023, 1:25:46 PM

28%
rgb-conv2d-304
PERFORMANCE
LATENCY
258 ms of 100 ms
Exceeds target by 158 ms
RAM
50 kB of 128 kB
ROM
46 kB of 1024 kB
DSP NN Unused
IMAGE INPUT

64 |
64

IMAGE

RGB

ACCURACY (KERAS)
CLASSIFICATION

0.0005 | 10 | 28%

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

4/18/2023, 1:26:04 PM

22%
rgb-mobilenetv1-871
PERFORMANCE
LATENCY
246 ms of 100 ms
Exceeds target by 146 ms
RAM
64 kB of 128 kB
ROM
114 kB of 1024 kB
DSP NN Unused
IMAGE INPUT

96 |
96

IMAGE

RGB

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

0.0005 | 20 | 22%

MobileNetV1 0.1
64 | 0.5

4/18/2023, 1:25:58 PM

14%
rgb-mobilenetv1-f8d
PERFORMANCE
LATENCY
246 ms of 100 ms
Exceeds target by 146 ms
RAM
64 kB of 128 kB
ROM
114 kB of 1024 kB
DSP NN Unused
IMAGE INPUT

96 |
96

IMAGE

RGB

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

0.0005 | 20 | 14%

MobileNetV1 0.1
64 | 0.5 |

4/18/2023, 1:28:05 PM

rgb-conv2d-a92
PERFORMANCE
LATENCY
183 ms of 100 ms
Exceeds target by 83 ms
RAM
27 kB of 128 kB
ROM
40 kB of 1024 kB
DSP NN Unused
IMAGE INPUT

32 |
32

IMAGE

RGB

ACCURACY (KERAS)
CLASSIFICATION

0.0005 | 10 | 0%

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

4/18/2023, 1:25:53 PM

rgb-mobilenetv1-8bf
PERFORMANCE
LATENCY
145 ms of 100 ms
Exceeds target by 45 ms
RAM
64 kB of 128 kB
ROM
109 kB of 1024 kB
DSP NN Unused
IMAGE INPUT

96 |
96

IMAGE

RGB

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

0.0005 | 20 | 0%

MobileNetV1 0.1
16 | 0.5 |

4/18/2023, 1:28:15 PM

rgb-mobilenetv1-8cb
PERFORMANCE
LATENCY
244 ms of 100 ms
Exceeds target by 144 ms
RAM
64 kB of 128 kB
ROM
109 kB of 1024 kB
DSP NN Unused
IMAGE INPUT

96 |
96

IMAGE

RGB

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

0.0005 | 20 | 0%

MobileNetV1 0.1
16 | 0.1

4/18/2023, 1:30:17 PM

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