Sparse ternary connect: Convolutional neural networks using ternarized weights with enhanced sparsity

Canran Jin, Heming Sun, Shinji Kimura

Abstract: Convolutional Neural Networks (CNNs) are indispensable in a wide range of tasks to achieve state-of-the-art results. In this work, we exploit ternary weights in both inference and training of CNNs and further propose Sparse Ternary Connect (STC) where kernel weights in float value are converted to 1, -1 and 0 based on a new conversion rule with the controlled ratio of 0. STC can save hardware resource a lot with small degradation of precision. The experimental evaluation on 2 popular datasets (CIFAR-10 and SVHN) shows that the proposed method can reduce resource utilization (by 28.9% of LUT, 25.3% of FF, 97.5% of DSP and 88.7% of BRAM on Xilinx Kintex-7 FPGA) with less than 0.5% accuracy loss.