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Spiking Neural Network library based on Tensorflow & Keras

Sequence (?)

model
DNN inference
DNN inference w/ fused-bn
Save activation distributions for data-based weight normalization
SNN inference

model configurations

DNN inference

fused bn

data-based weight normalization

using training dataset defalut: outlier-robust layer-wise normalization refs -

SNN inference

only with fused-bn

Pretrained models (internal share, include trained conditions)

CIFAR10

Models	Para.	Acc.	Server	Best Model	Note
VGG16	15.27M	94.70
----	----	----	----	----	----
ResNet20	4.34M	96.16	NIPA
ResNet32	7.45M	96.45	NIPA
ResNet44	10.55M	96.68	NIPA
ResNet56	13.66M	96.32	NIPA		ep-1000, lr-step-200
----	----	----	----	----	----
ResNet18	11.19M	95.96	NIPA
ResNet34	21.31M	96.22	NIPA
ResNet50	23.60M	95.44	NIPA
----	----	----	----	----	----
ResNet20V2	4.34M	96.26	NIPA
ResNet32V2	7.45M	96.36	NIPA
ResNet44V2	10.55M	96.34	NIPA
ResNet56V2	13.66M	96.69	NIPA
----	----	----	----	----	----
ResNet18V2	11.19M	95.49	NIPA
ResNet34V2	21.31M	96.07	NIPA
ResNet50V2	23.61M	95.12	NIPA

CIFAR-100

Models	Para.	Acc.	Server	Best Model	Note
VGG16	15.31M	72.47		ep-300_bat-100_opt-SGD_lr-step-5E-04_lmb-1E-05_sc_cm
ResNet20	4.36M	75.05	NIPA
ResNet32	7.47M	75.80	NIPA
ResNet44	10.58M	76.03	NIPA
ResNet56	13.68M	77.27	NIPA
----	----	----	----	----	----
ResNet18	11.24M	72.33	NIPA
ResNet34	21.36M	73.01	NIPA
ResNet50	23.78M	72.37	NIPA	ep-1000, lr-step-200
----	----	----	----	----	----
ResNet20V2	4.37M	75.02	NIPA	ep-1000, lr-step-200
ResNet32V2	7.47M	76.06	NIPA
ResNet44V2	10.58M	76.27	NIPA	ep-1000, lr-step-200
ResNet56V2	13.68M	76.82	NIPA	ep-1000, lr-step-200
----	----	----	----	----	----
ResNet18V2	11.24M	71.60	NIPA
ResNet34V2	21.36M	73.65	NIPA
ResNet50V2	23.79M	71.25	NIPA	ep-1000, lr-step-200

ImageNet (from Keras)

Models	Para.	Acc.	Acc.(top5)
VGG16
ResNet50
ResNet101	44.7M	76.31	92.76
ResNet152	60.4M	76.58	93.13
MobileNetV2	3.5M	72.01	90.60
EfficientNetV2S	21.6M	83.92	96.73

ImageNet

Pretrained models

VGG - ep-1000_bat-100_opt-SGD_lr-STEP-5E-03_lmb-1E-05_sc_cm ResNet - ep-300_bat-100

Name		Name	Last commit message	Last commit date
Latest commit History 946 Commits
MNIST-data		MNIST-data
__pycache__		__pycache__
autokeras_custom		autokeras_custom
configs		configs
datasets		datasets
keras_cv_local		keras_cv_local
layers_new		layers_new
lib_snn		lib_snn
models		models
ops		ops
script		script
stat/test		stat/test
tensornets		tensornets
.gitignore		.gitignore
ILVRC2012_training_labels		ILVRC2012_training_labels
ILVRC2012_training_synsets		ILVRC2012_training_synsets
LICENSE		LICENSE
README.md		README.md
akTest.py		akTest.py
ak_HW_MNIST.py		ak_HW_MNIST.py
backprop.py		backprop.py
callbacks.py		callbacks.py
config.py		config.py
config_dnn-to-snn.py		config_dnn-to-snn.py
config_loss_vis.py		config_loss_vis.py
config_snn_inference.py		config_snn_inference.py
config_snn_nas.py		config_snn_nas.py
config_snn_training.py		config_snn_training.py
config_snn_training_H_direct.py		config_snn_training_H_direct.py
config_snn_training_WTA_SNN.py		config_snn_training_WTA_SNN.py
config_snn_training_heb.py		config_snn_training_heb.py
config_snn_training_imagenet.py		config_snn_training_imagenet.py
config_snn_training_neuro_dev.py		config_snn_training_neuro_dev.py
config_snn_training_sptr.py		config_snn_training_sptr.py
config_snn_training_surro.py		config_snn_training_surro.py
config_tutorial_grad_cam.py		config_tutorial_grad_cam.py
config_xai.py		config_xai.py
data.py		data.py
flags.py		flags.py
layers_tensornets.py		layers_tensornets.py
main.py		main.py
main_ak_load.py		main_ak_load.py
main_ak_train_SNN.py		main_ak_train_SNN.py
main_dnn_to_snn.py		main_dnn_to_snn.py
main_ev_algo.py		main_ev_algo.py
main_ev_algo_og.py		main_ev_algo_og.py
main_hp_tune.py		main_hp_tune.py
main_make_imagenet_model_from_keras.py		main_make_imagenet_model_from_keras.py
main_multimodal.py		main_multimodal.py
main_nas.py		main_nas.py
main_snn_conversion.py		main_snn_conversion.py
main_snn_train_sptr.py		main_snn_train_sptr.py
main_snn_training.py		main_snn_training.py
main_snn_training_H-direct.py		main_snn_training_H-direct.py
main_snn_training_WTA_SNN.py		main_snn_training_WTA_SNN.py
main_snn_training_neuro_dev.py		main_snn_training_neuro_dev.py
main_snn_training_surro.py		main_snn_training_surro.py
main_transform_model.py		main_transform_model.py
main_xai.py		main_xai.py
merge_files_neuron_importance.py		merge_files_neuron_importance.py
model_cnn_mnist.py		model_cnn_mnist.py
requirements.txt		requirements.txt
resnet.py		resnet.py
resnet_v2.py		resnet_v2.py
run.sh		run.sh
run_batch.sh		run_batch.sh
run_batch_WTA_SNN_anal_neuron_imp.sh		run_batch_WTA_SNN_anal_neuron_imp.sh
run_batch_enc_dec_tk.sh		run_batch_enc_dec_tk.sh
run_batch_enc_dec_tk_cpu.sh		run_batch_enc_dec_tk_cpu.sh
run_batch_reg_spike.sh		run_batch_reg_spike.sh
run_batch_train_snn.sh		run_batch_train_snn.sh
run_batch_train_snn_1.sh		run_batch_train_snn_1.sh
run_calib.sh		run_calib.sh
run_calib_batch.sh		run_calib_batch.sh
run_calibration_idx_test_batch.sh		run_calibration_idx_test_batch.sh
run_console_log.sh		run_console_log.sh
run_dynamic_bn_scale_search.sh		run_dynamic_bn_scale_search.sh
run_enc_dec_tk.sh		run_enc_dec_tk.sh
run_enc_dec_tk_cpu.sh		run_enc_dec_tk_cpu.sh
run_grad_cam.sh		run_grad_cam.sh
run_noise.sh		run_noise.sh
run_noise_batch.sh		run_noise_batch.sh
run_old.sh		run_old.sh
run_test_imagenet_infer_batch.sh		run_test_imagenet_infer_batch.sh
run_train.sh		run_train.sh
run_train_batch.sh		run_train_batch.sh
run_train_snn.sh		run_train_snn.sh
run_train_tc_batch.sh		run_train_tc_batch.sh
run_vth_search_idx_test_batch.sh		run_vth_search_idx_test_batch.sh
run_vth_toggle.sh		run_vth_toggle.sh
run_vth_toggle_batch.sh		run_vth_toggle_batch.sh
tensorboard.sh		tensorboard.sh
test.py		test.py
test_speck_dev.py		test_speck_dev.py
train.py		train.py
train_snn.py		train_snn.py
train_w_prof.py		train_w_prof.py
tutorial_grad_cam.py		tutorial_grad_cam.py
tutorial_loss_vis.py		tutorial_loss_vis.py
utils.py		utils.py

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Sequence (?)

model configurations

DNN inference

fused bn

data-based weight normalization

SNN inference

Pretrained models (internal share, include trained conditions)

Pretrained models

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Sequence (?)

model configurations

DNN inference

fused bn

data-based weight normalization

SNN inference

Pretrained models (internal share, include trained conditions)

Pretrained models

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