Table 1.

Structure of convolution networks used in this work

1/2 chip	1 chip	2 chip	4 chip
S-12	S-16	S-32	S-64
P4-128 (4)	P4-252 (2)	S-128 (4)	S-256 (8)
D	N-256 (2)	N-128 (1)	N-256 (2)
S-256 (16)	P-256 (8)	P-128 (4)	P-256 (8)
N-256 (2)	S-512 (32)	S-256 (16)	S-512 (32)
P-512 (16)	N-512 (4)	N-256 (2)	N-512 (4)
S-1020 (4)	N-512 (4)	P-256 (8)	P-512 (16)
(6,528/class)	N-512 (4)	S-512 (32)	S-1024 (64)
	P-512 (16)	N-512 (4)	N-1024 (8)
	S-1024 (64)	P-512 (16)	P-1024 (32)
	N-1024 (8)	S-2048 (64)	S-2048 (128)
	P-1024 (32)	N-2048 (16)	N-2048 (16)
	N-1024 (8)	N-2048 (16)	N-2048 (16)
	N-1024 (8)	N-2048 (16)	N-2048 (16)
	N-2040 (8)	N-4096 (16)	N-4096 (16)
	(816/class)	(6,553/class)	(6,553/class)

Each layer is described as type-features (groups), where type can be S for spatial filter layers with filter size $3\times 3$ and stride 1, N for network-in-network layers with filter size $1\times 1$ and stride 1, P for convolutional pooling layer with filter size $2\times 2$ and stride 2, P4 for convolutional pooling layer with filter size $4\times 4$ and stride 2, and D for dropout layers. The number of output features assigned to each of the 10 CIFAR10 classes is indicated below the final layer as (features/class). The eight-chip network is the same as a four-chip network with twice as many features per layer.