Table 3.

Summary of results

Dataset	State of the art		TrueNorth best accuracy		TrueNorth 1 chip
	Approach	Accuracy	Accuracy	#cores	Accuracy	#cores	FPS	mW	FPS/W
CIFAR10	CNN (11)	$91.73\%$	$\mathbf{89.32}\mathrm{\%}$	31492	$\mathbf{83.41}\mathrm{\%}$	4042	1249	204.4	6108.6
CIFAR100	CNN (34)	$65.43\%$	$\mathbf{65.48}\mathrm{\%}$	31492	$\mathbf{55.64}\mathrm{\%}$	4042	1526	207.8	7343.7
SVHN	CNN (34)	$98.08\%$	$\mathbf{97.46}\mathrm{\%}$	31492	$\mathbf{96.66}\mathrm{\%}$	4042	2526	256.5	9849.9
GTSRB	CNN (35)	$99.46\%$	$\mathbf{97.21}\mathrm{\%}$	31492	$\mathbf{96.50}\mathrm{\%}$	4042	1615	200.6	8051.8
LOGO32	CNN	$93.70\%$	$\mathbf{90.39}\mathrm{\%}$	13606	$\mathbf{85.70}\mathrm{\%}$	3236	1775	171.7	10335.5
VAD	MLP (36)	$95.00\%$	$\mathbf{97.00}\mathrm{\%}$	1758	$\mathbf{95.42}\mathrm{\%}$	423	1539	26.1	59010.7
TIMIT Class.	HGMM (37)	$83.30\%$	$\mathbf{82.18}\mathrm{\%}$	8802	$\mathbf{79.16}\mathrm{\%}$	1943	2610	142.6	18300.1
TIMIT Frames	BLSTM (38)	$72.10\%$	$\mathbf{73.46}\mathrm{\%}$	20038	$\mathbf{71.17}\mathrm{\%}$	2476	2107	165.9	12698.0

The network for LOGO32 was an internal implementation. BLSTM, bidirectional long short-term memory; CNN, convolutional neural network; FPS, frames/second; FPS/W, fames/second per Watt; HGMM, hierarchical Gaussian mixture model; MLP, multilayer perceptron. Accuracy of TrueNorth networks is shown in bold.