ML-mini-project - Impulses

Experiments (3 / 3)

Name	Input	DSP Blocks	Learn Blocks	f32_V_Acc	f32_T_Acc	i8_V_Acc	i8_T_Acc	f32_Latency	f32_Flash	i8_Latency	i8_Flash
Impulse #1	1,000ms.	MFE	Classifier	93.0%	86.9%	94.4%	-	5 ms.	36.0K	8 ms.	33.8K	View impulse
mfe-conv1d-3e5	1,000ms.	Audio (MFE)	Classifier	89.3%	83.5%	88.5%	-	1 ms.	64.6K	4 ms.	43.1K	View impulse
Impulse #2	1,000ms.	Audio (MFE)	Classifier	82.0%	82.6%	82.0%	82.6%	4 ms.	576.5K	81 ms.	178.3K	View impulse

Impulse information

Tags - Impulse tags

Source - Impulse source (Manual vs EON Tuner)

Created by - The user who originally created this impulse

Key metrics

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_Flash - float32 Flash for the complete impulse.

i8_Latency - int8 Latency for the complete impulse.

i8_Flash - int8 Flash for the complete impulse.

Input block config

Win_Size - Window size

Win_Incr - Window increase

Freq - Frequency

Processing block config

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)

Noi_flo - Normalization > Noise floor (dB) (Processing Block #1)

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

Learn block config

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)

Val_Key - Split train/validation set on metadata key (Learn Block #1)

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

Accuracy metrics

f32_V_AUC - float32 Area under ROC Curve (validation set)

f32_V_WA_P - float32 Weighted average Precision (validation set)

f32_V_WA_R - float32 Weighted average Recall (validation set)

f32_V_WA_F1 - float32 Weighted average F1 score (validation set)

f32_T_AUC - float32 Area under ROC Curve (test set)

f32_T_WA_P - float32 Weighted average Precision (test set)

f32_T_WA_R - float32 Weighted average Recall (test set)

f32_T_WA_F1 - float32 Weighted average F1 score (test set)

i8_V_AUC - int8 Area under ROC Curve (validation set)

i8_V_WA_P - int8 Weighted average Precision (validation set)

i8_V_WA_R - int8 Weighted average Recall (validation set)

i8_V_WA_F1 - int8 Weighted average F1 score (validation set)

i8_T_AUC - int8 Area under ROC Curve (test set)

i8_T_WA_P - int8 Weighted average Precision (test set)

i8_T_WA_R - int8 Weighted average Recall (test set)

i8_T_WA_F1 - int8 Weighted average F1 score (test set)

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

Experiments (3 / 3)

Impulse #1

mfe-conv1d-3e5

Impulse #2