This paper reports our efforts on swCaffe, a highly efficient parallel framework for accelerating deep neural networks (DNNs) training on Sunway TaihuLight, the current fastest supercomputer in the world that adopts a unique many-core heterogeneous architecture, with 40,960 SW26010 processors connected through a customized communication network.First, we point out some insightful principles to fully exploit the performance of the innovative many-core architecture.Second, we propose a set of optimization strategies for redesigning a variety of neural network layers based on Caffe.Third, we put forward a topology-aware parameter synchronization scheme to scale the synchronous Stochastic Gradient Descent (SGD) method to multiple processors efficiently.We evaluate our framework by training a variety of widely used neural networks with the ImageNet dataset.On a single node, swCaffe can achieve 23%˜119% overall performance compared with Caffe running on K40m GPU.As compared with the Caffe on CPU, swCaffe runs 3.04˜7.84xfaster on all the networks.Finally, we present the scalability of swCaffe for training of ResNet-50 and AlexNet on the scale of 1024 nodes.