A fine-grained mixed precision DNN accelerator using a two-stage big–little core RISC-V MCU

Abstract

Deep neural networks (DNNs) are widely used in modern AI systems, and their dedicated accelerators have become a promising option for edge scenarios due to the energy efficiency and high performance. Since the DNN model requires significant storage and computation resources, various energy efficient DNN accelerators or algorithms have been proposed for edge devices. Many quantization algorithms for efficient DNN training have been proposed, where the weights in the DNN layers are quantified to small/zero values, therefore requiring much fewer effective bits, i.e., low-precision bits in both arithmetic and storage units. Such sparsity can be leveraged to remove non-effective bits and reduce the design cost, however at the cost of accuracy degradation, as some key operations still demand a higher precision. Therefore, in this paper, we propose a universal mixed-precision DNN accelerator architecture that can simultaneously support mixed-precision DNN arithmetic operations. A big–little core controller based on RISC-V is implemented to effectively control the datapath, and assign the arithmetic operations to full precision and low precision process units, respectively. Experimental results show that, with the proposed designs, we can save 16% chip area and 45.2% DRAM access compared with the state-of-the-art design.