A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural Network
Ruichen Ma Guanchao Qiao Yian Liu Liwei Meng Ning Ning Yang Liu Shaogang Hu
Binary neural networks utilize 1-bit quantized weights and activations to
reduce both the model's storage demands and computational burden. However,
advanced binary architectures still incorporate millions of inefficient and
nonhardware-friendly full-precision multiplication operations. A&B BNN is
proposed to directly remove part of the multiplication operations in a
traditional BNN and replace the rest with an equal number of bit operations,
introducing the mask layer and the quantized RPReLU structure based on the
normalizer-free network architecture. The mask layer can be removed during
inference by leveraging the intrinsic characteristics of BNN with
straightforward mathematical transformations to avoid the associated
multiplication operations. The quantized RPReLU structure enables more
efficient bit operations by constraining its slope to be integer powers of 2.
Experimental results achieved 92.30%, 69.35%, and 66.89% on the CIFAR-10,
CIFAR-100, and ImageNet datasets, respectively, which are competitive with the
state-of-the-art. Ablation studies have verified the efficacy of the quantized
RPReLU structure, leading to a 1.14% enhancement on the ImageNet compared to
using a fixed slope RLeakyReLU. The proposed add&bit-operation-only BNN offers
an innovative approach for hardware-friendly network architecture.