Towards Efficient Microarchitectural Design for Accelerating Unsupervised GAN-Based Deep Learning

Recently, deep learning based approaches have emerged as indispensable tools to perform big data analytics. Normally, deep learning models are first trained with a supervised method and then deployed to execute various tasks. The supervised method involves extensive human efforts to collect and label the large-scale dataset, which becomes impractical in the big data era where raw data is largely un-labeled and uncategorized. Fortunately, the adversarial learning, represented by Generative Adversarial Network (GAN), enjoys a great success on the unsupervised learning. However, the distinct features of GAN, such as massive computing phases and non-traditional convolutions challenge the existing deep learning accelerator designs. In this work, we propose the first holistic solution for accelerating the unsupervised GAN-based Deep Learning. We overcome the above challenges with an algorithm and architecture co-design approach. First, we optimize the training procedure to reduce on-chip memory consumption. We then propose a novel time-multiplexed design to efficiently map the abundant computing phases to our microarchitecture. Moreover, we design high-efficiency dataflows to achieve high data reuse and skip the zero-operand multiplications in the non-traditional convolutions. Compared with traditional deep learning accelerators, our proposed design achieves the best performance (average 4.3X) with the same computing resource. Our design also has an average of 8.3X speedup over CPU and 6.2X energy-efficiency over NVIDIA GPU.