Table 1.

Structure of convolution networks used in this work

1/2 chip1 chip2 chip4 chip
S-12S-16S-32S-64
P4-128 (4)P4-252 (2)S-128 (4)S-256 (8)
DN-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 3×3 and stride 1, N for network-in-network layers with filter size 1×1 and stride 1, P for convolutional pooling layer with filter size 2×2 and stride 2, P4 for convolutional pooling layer with filter size 4×4 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.