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Mateusz / CBM Fault Classifier Public

Clone this project

Mateusz / CBM Fault Classifier Public

Target: Nordic nRF9160 DK (Cortex-M33 64MHz) 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

Nordic nRF9160 DK (Cortex-M33 64MHz)

100 ms

256 kB

1024 kB

Filters

Status

Pending 0

Running 0

Completed 30

Failed 0

DSP type

Spectral analysis 30

Model type

Dense 30

View

Data set

Validation

Test

Variant

Sort

General

Accuracy

Latency

RAM

ROM

Last updated

F1-score

Precision

Recall

80%

spectr-dense-f2e

PERFORMANCE

LATENCY

44 ms of 100 ms

RAM

31 kB of 256 kB

ROM

82 kB of 1024 kB

DSP NN Unused

TIME-SERIES INPUT

1000 ms |

250 ms |

Enabled

SPECTRAL-ANALYSIS

1024

ACCURACY (KERAS)

4/28/2022, 11:53:36 PM

80%

spectr-dense-6e1

PERFORMANCE

LATENCY

54 ms of 100 ms

RAM

32 kB of 256 kB

ROM

83 kB of 1024 kB

DSP NN Unused

TIME-SERIES INPUT

1000 ms |

1000 ms |

Enabled

SPECTRAL-ANALYSIS

1024

ACCURACY (KERAS)

4/29/2022, 12:05:58 AM

80%

spectr-dense-0cd

PERFORMANCE

LATENCY

44 ms of 100 ms

RAM

33 kB of 256 kB

ROM

79 kB of 1024 kB

DSP NN Unused

TIME-SERIES INPUT

1000 ms |

250 ms |

Enabled

SPECTRAL-ANALYSIS

1024

ACCURACY (KERAS)

4/29/2022, 12:04:07 AM

80%

spectr-dense-029

PERFORMANCE

LATENCY

38 ms of 100 ms

RAM

31 kB of 256 kB

ROM

82 kB of 1024 kB

DSP NN Unused

TIME-SERIES INPUT

1000 ms |

500 ms |

Enabled

SPECTRAL-ANALYSIS

1024

ACCURACY (KERAS)

4/29/2022, 12:04:19 AM

79%

spectr-dense-3db

PERFORMANCE

LATENCY

56 ms of 100 ms

RAM

16 kB of 256 kB

ROM

82 kB of 1024 kB

DSP NN Unused

TIME-SERIES INPUT

1000 ms |

250 ms |

Enabled

SPECTRAL-ANALYSIS

128

ACCURACY (KERAS)

4/28/2022, 11:57:17 PM

78%

spectr-dense-b30

PERFORMANCE

LATENCY

46 ms of 100 ms

RAM

32 kB of 256 kB

ROM

83 kB of 1024 kB

DSP NN Unused

TIME-SERIES INPUT

1000 ms |

250 ms |

Enabled

SPECTRAL-ANALYSIS

1024

ACCURACY (KERAS)

4/29/2022, 12:00:20 AM

78%

spectr-dense-837

PERFORMANCE

LATENCY

42 ms of 100 ms

RAM

32 kB of 256 kB

ROM

83 kB of 1024 kB

DSP NN Unused

TIME-SERIES INPUT

1000 ms |

500 ms |

Enabled

SPECTRAL-ANALYSIS

1024

ACCURACY (KERAS)

4/29/2022, 12:01:22 AM

78%

spectr-dense-d7a

PERFORMANCE

LATENCY

42 ms of 100 ms

RAM

31 kB of 256 kB

ROM

82 kB of 1024 kB

DSP NN Unused

TIME-SERIES INPUT

1000 ms |

1000 ms |

Enabled

SPECTRAL-ANALYSIS

1024

ACCURACY (KERAS)

4/28/2022, 11:54:57 PM

78%

spectr-dense-8e9

PERFORMANCE

LATENCY

28 ms of 100 ms

RAM

31 kB of 256 kB

ROM

82 kB of 1024 kB

DSP NN Unused

TIME-SERIES INPUT

1000 ms |

250 ms |

Enabled

SPECTRAL-ANALYSIS

1024

ACCURACY (KERAS)

4/29/2022, 12:05:33 AM

76%

spectr-dense-10c

PERFORMANCE

LATENCY

38 ms of 100 ms

RAM

16 kB of 256 kB

ROM

50 kB of 1024 kB

DSP NN Unused

TIME-SERIES INPUT

1000 ms |

250 ms |

Enabled

SPECTRAL-ANALYSIS

128

ACCURACY (KERAS)

4/28/2022, 11:54:38 PM

76%

spectr-dense-e19

PERFORMANCE

LATENCY

44 ms of 100 ms

RAM

16 kB of 256 kB

ROM

50 kB of 1024 kB

DSP NN Unused

TIME-SERIES INPUT

1000 ms |

500 ms |

Enabled

SPECTRAL-ANALYSIS

128

ACCURACY (KERAS)

4/28/2022, 11:56:15 PM

76%

spectr-dense-753

PERFORMANCE

LATENCY

34 ms of 100 ms

RAM

16 kB of 256 kB

ROM

50 kB of 1024 kB

DSP NN Unused

TIME-SERIES INPUT

1000 ms |

1000 ms |

Enabled

SPECTRAL-ANALYSIS

128

ACCURACY (KERAS)

4/29/2022, 12:02:54 AM

73%

spectr-dense-f92

PERFORMANCE

LATENCY

10 ms of 100 ms

RAM

16 kB of 256 kB

ROM

34 kB of 1024 kB

DSP NN Unused

TIME-SERIES INPUT

1000 ms |

500 ms |

Enabled

SPECTRAL-ANALYSIS

128

ACCURACY (KERAS)

4/29/2022, 12:04:27 AM

70%

spectr-dense-11b

PERFORMANCE

LATENCY

22 ms of 100 ms

RAM

31 kB of 256 kB

ROM

34 kB of 1024 kB

DSP NN Unused

TIME-SERIES INPUT

1000 ms |

1000 ms |

Enabled

SPECTRAL-ANALYSIS

1024

ACCURACY (KERAS)

4/28/2022, 11:57:31 PM

68%

spectr-dense-4b3

PERFORMANCE

LATENCY

40 ms of 100 ms

RAM

16 kB of 256 kB

ROM

82 kB of 1024 kB

DSP NN Unused

TIME-SERIES INPUT

1000 ms |

500 ms |

Enabled

SPECTRAL-ANALYSIS

128

ACCURACY (KERAS)

4/28/2022, 11:54:54 PM

Training output

Hide

Click 'Run EON Tuner' to begin

EON Tuner settings

Find the optimal architecture for your machine learning model

The EON™ Tuner will evaluate many candidate model architectures (selected based on your target device and latency requirements) concurrently to help you find the best performing architecture for your application.

The search process can take up to 6 hours to complete. While the EON Tuner is running you can view the progress on this page at any time.

Name

Run #1:

Search space configuration

Configure the search space to narrow down your EON Tuner results. Include multiple parameter values to search for different combinations. To start, select a template below, a previous search space configuration, or add blocks manually.

Continuous motion

Classification

Detect cases where a motion is constantly playing.

Classification & anomaly

Detect cases where a motion is constantly playing

Motion events

Classification

Listen for short one-time occurances of motion

No previous runs available

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

ORGANIZATION (ML)

Add custom ML block

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?