Table 8.

#Spikes/Image inference and spike efficiency comparison between SNN and ANN-SNN converted networks for benchmark datasets trained on different network models.

Dataset	Model	Spike/Image			Spike efficiency compared to
		SNN	ANN-SNN	ANN-SNN	ANN-SNN	ANN-SNN
			(Diehl et al., 2015)	(Sengupta et al., 2019)	(Diehl et al., 2015)	(Sengupta et al., 2019)
MNIST	LeNet	5.52E+04	3.4E+04	2.9E+04	0.62x	0.53x
				7.3E+04		1.32x
SVHN	VGG7	5.56E+06	3.7E+06	1.0E+07	0.67x	1.84x
			1.9E+07	1.7E+07	3.40x	2.99x
	ResNet7	4.66E+06	3.9E+06	3.1E+06	0.85x	0.67x
			2.4E+07	2.0E+07	5.19x	4.30x
CIFAR-10	VGG9	1.24E+06	1.6E+06	2.2E+06	1.32x	1.80x
			8.3E+06	9.6E+06	6.68x	7.78x
	ResNet9	4.32E+06	2.7E+06	1.5E+06	0.63x	0.35x
			1.0E+07	7.8E+06	2.39x	1.80x
	ResNet11	1.53E+06	9.7E+06	1.8E+06	6.33x	1.17x
				9.2E+06		5.99x

(For each network, the 1st row corresponds to iso-accuracy and the 2nd row corresponds to maximum-accuracy condition).