XOR-Net : an efficient computation pipeline for binary neural network inference on edge devices - Zhu, Shien (2020)
Accelerating the inference of Convolution Neural Networks (CNNs) on edge devices is essential due to the small memory size and poor computation capability of these devices. Network quantization methods such as XNOR-Net, Bi-Real-Net, and XNOR-Net++ reduce the memory usage of CNNs by binarizing the CNNs. They also simplify the multiplication operations to bit-wise operations and obtain good speedup on edge devices. However, there are hidden redundancies in the computation pipeline of these methods, constraining the speedup of those binarized CNNs.