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Target: Raspberry Pi 4 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

Raspberry Pi 4

100 ms

8388608 kB

33554432 kB

Filters

Status

Pending 0

Running 0

Completed 30

Failed 0

DSP type

RGB 30

Model type

FOMO-AD 30

View

Data set

Validation

Test

Variant

Sort

General

Accuracy

Latency

RAM

ROM

Last updated

Anomaly accuracy

Overall

Balanced

Anomaly

No anomaly

F1-score

Precision

Recall

Run #2 (Apr 23 2024, 14:30:15, finished)

Run #3 - Raspberry Pi (May 10 2024, 12:04:04, finished) Run #2 (Apr 23 2024, 14:30:15, finished) Run #1 (Apr 23 2024, 13:32:39, finished)

90%

rgb-fomo-ad-8e1

PERFORMANCE

LATENCY

47 ms of 100 ms

RAM

2433 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

320 |

320

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 7.1

90% | - | - | -

4/23/2024, 2:52:41 PM

90%

rgb-fomo-ad-c8a

PERFORMANCE

LATENCY

48 ms of 100 ms

RAM

2433 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

320 |

320

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 11.1

90% | - | - | -

4/23/2024, 2:47:35 PM

90%

rgb-fomo-ad-c69

PERFORMANCE

LATENCY

34 ms of 100 ms

RAM

1209 kB of 8388608 kB

ROM

70 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

224 |

224

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.35 | 11.1

90% | - | - | -

4/23/2024, 2:39:12 PM

87%

rgb-fomo-ad-c95

PERFORMANCE

LATENCY

62 ms of 100 ms

RAM

2437 kB of 8388608 kB

ROM

70 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

320 |

320

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.35 | 7.1

87% | - | - | -

4/23/2024, 2:48:58 PM

87%

rgb-fomo-ad-14e

PERFORMANCE

LATENCY

79 ms of 100 ms

RAM

2437 kB of 8388608 kB

ROM

70 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

320 |

320

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.35 | 5.1

87% | - | - | -

4/23/2024, 2:38:09 PM

84%

rgb-fomo-ad-6e8

PERFORMANCE

LATENCY

46 ms of 100 ms

RAM

2433 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

320 |

320

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 11.1

84% | - | - | -

4/23/2024, 2:56:50 PM

84%

rgb-fomo-ad-191

PERFORMANCE

LATENCY

51 ms of 100 ms

RAM

2433 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

320 |

320

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 5.1

84% | - | - | -

4/23/2024, 2:56:55 PM

82%

rgb-fomo-ad-3dd

PERFORMANCE

LATENCY

35 ms of 100 ms

RAM

1209 kB of 8388608 kB

ROM

70 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

224 |

224

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.35 | 5.1

82% | - | - | -

4/23/2024, 2:54:56 PM

82%

rgb-fomo-ad-b76

PERFORMANCE

LATENCY

32 ms of 100 ms

RAM

1209 kB of 8388608 kB

ROM

70 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

224 |

224

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.35 | 7.1

82% | - | - | -

4/23/2024, 2:49:48 PM

82%

rgb-fomo-ad-775

PERFORMANCE

LATENCY

29 ms of 100 ms

RAM

1205 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

224 |

224

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 5.1

82% | - | - | -

4/23/2024, 2:49:50 PM

82%

rgb-fomo-ad-a29

PERFORMANCE

LATENCY

25 ms of 100 ms

RAM

1205 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

224 |

224

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 7.1

82% | - | - | -

4/23/2024, 2:43:44 PM

82%

rgb-fomo-ad-7e8

PERFORMANCE

LATENCY

35 ms of 100 ms

RAM

1209 kB of 8388608 kB

ROM

70 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

224 |

224

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.35 | 11.1

82% | - | - | -

4/23/2024, 2:38:59 PM

82%

rgb-fomo-ad-cf6

PERFORMANCE

LATENCY

29 ms of 100 ms

RAM

1205 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

224 |

224

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 7.1

82% | - | - | -

4/23/2024, 2:34:22 PM

81%

rgb-fomo-ad-d2a

PERFORMANCE

LATENCY

62 ms of 100 ms

RAM

2433 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

320 |

320

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 7.1

81% | - | - | -

4/23/2024, 2:43:41 PM

81%

rgb-fomo-ad-fc1

PERFORMANCE

LATENCY

41 ms of 100 ms

RAM

2433 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

320 |

320

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 11.1

81% | - | - | -

4/23/2024, 2:37:40 PM

Training output

Hide

Click 'Run EON Tuner' to begin

Configure a new EON Tuner run

The EON Tuner uses a Bayesian search algorithm to find optimal impulse configurations. To get started, select at least one run objective and configure your search space.

Name

Compute limit

Run objectives

Search space configuration

USE CASE

Continuous motion | Classification Continuous motion | Classification & anomaly Motion events | Classification

TUNER CONFIGURATION

Run #3 - Raspberry Pi (May 10), finished Run #2 (Apr 23), finished Run #1 (Apr 23), finished

IMPULSE CONFIGURATION

Impulse #1

INPUT

Time-series
Pre-processed features

DSP

Raw
Spectral analysis
IMU (Syntiant)

LEARN

Dense
Convolutional (1D)

ANOMALY

Anomaly (k-means)
Anomaly (GMM)

ORGANIZATION (DSP)

Add custom DSP block
Add custom ML block

Custom search space

Do more with EON Tuner

Access more parallel EON Tuner jobs for faster results, a fully customizable search space, and more model variant options.

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Configure your target device and application budget

Target device

Define your target device requirements to inform model optimizations and performance calculations. No device yet? Use the default settings which you can change at any time.

Target device

Processor type

Processor family

Processor architecture

Clock rate

| MHz

Max

Accelerator

Device ID (optional)

Custom device name (optional)

Application budget

Specify the available RAM and ROM for the model's operation, along with the maximum allowed latency for your specific application. Not sure yet? Start with the defaults and modify them later on.

RAM

| KB

Max

ROM

| KB

Max

Latency

| ms

Max

Choose your pricing

YEARLY

$400 /month

billed annually

SAVE 15%

MONTHLY

$475 /month

billed monthly

Additional usage

Professional Plan includes 1,000 compute minutes per month. Additional usage will be charged at $0.10 per minute, billed monthly.

Upgrade to Enterprise

Why is this the right time for your team to invest in edge AI? What best describes your use case? What is your expected timeline for solving your problem?

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