Tad McAllister / alarm-detection Public

Experiments (2 / 10)

	Name	Input	DSP Blocks	Learn Blocks	f32_V_Acc	f32_T_Acc	i8_V_Acc	i8_T_Acc	f32_Latency	f32_RAM	f32_Flash	i8_Latency	i8_RAM	i8_Flash
	Time series data, Audio (MFE), Neural Network (Keras) (+syntiant block)	5,000ms.	MFE	NN Classifier	100.0%	-	100.0%	-	64 ms.	16.3K	34.8K	46 ms.	16.3K	30.6K	View impulse
	Time series data, Audio (Syntiant), Neural Network (Keras) (+syntiant block)	5,000ms.	Syntiant	NN Classifier	-	-	-	-	-	-	-	-	-	-	View impulse

Select which columns to show in the impulse list:

Source - Impulse source (Manual vs EON Tuner)

Created by - The user who originally created this impulse

f32_V_Acc - Accuracy (float32, validation set)

f32_T_Acc - Accuracy (float32, test set)

i8_V_Acc - Accuracy (int8, validation set)

i8_T_Acc - Accuracy (int8, test set)

f32_Latency - float32 Latency for the complete impulse.

f32_RAM - float32 RAM for the complete impulse.

f32_Flash - float32 Flash for the complete impulse.

i8_Latency - int8 Latency for the complete impulse.

i8_RAM - int8 RAM for the complete impulse.

i8_Flash - int8 Flash for the complete impulse.

Win_Size - Window size

Win_Incr - Window increase

Zero_Pad - Zero pad data

Norm - Normalization

Fra_len - Mel-filterbank energy features > Frame length (Processing Block #1)

Fra_str - Mel-filterbank energy features > Frame stride (Processing Block #1)

Fil_num - Mel-filterbank energy features > Filter number (Processing Block #1)

FFT_len - Mel-filterbank energy features > FFT length (Processing Block #1)

Low_fre - Mel-filterbank energy features > Low frequency (Processing Block #1)

Hig_fre - Mel-filterbank energy features > High frequency (Processing Block #1)

Win_siz - Normalization > Window size (Processing Block #1)

Coe - Preemphasis > Coefficient (Processing Block #1)

Fea_ext - Chip > Features extractor (Processing Block #1)

Model - ML Model architecture (Learn Block #1)

Tc - Training cycles (Learn Block #1)

Lr - Learning rate (Learn Block #1)

Lo - Use learned optimizer? (Learn Block #1)

Acw - Auto-weight classes (Learn Block #1)

Data_Aug - Data augmentation (Learn Block #1)

Val_Size - Validation set size (Learn Block #1)

Mode - Visual / expert mode (Learn Block #1)

Configure your target device for model performance calculations, and to enable target specific optimizations.

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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 family

Clock rate

| MHz

Max

Custom device name

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

YEARLY

$400 /month

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MONTHLY

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Experiments (2 / 10)

Time series data, Audio (MFE), Neural Network (Keras) (+syntiant block)

Time series data, Audio (Syntiant), Neural Network (Keras) (+syntiant block)