In this article, a technique, based on using Residue Number System (RNS) is suggested to improve the energy efficiency of Deep Neural Networks (DNNs). In the DNN architecture, which is fully RNS-based, only weights and the primary inputs in the main memory are in the binary number system (BNS). The architecture, which is called Res-DNN, offers a high energy saving while requiring higher bit count for data to handle the overflow compared to that of a BNS one. Scaling techniques in the processing elements are employed in the RNS-based computations to make the computation bit widths the same as the BNS bit width. In this architecture, the MAX pooling and ReLU activation function are implemented in the RNS format. To lower the memory usage and required memory bandwidth, we suggest a Huffman-based coding. Additionally, for accessing the weights stored in the main memory, to obtain further energy reduction, we propose a structural modification to the memory hierarchy where a lower level register file is added to the data path of these accesses. The effectiveness of the proposed architecture is evaluated under seven state-of-the-art DNNs with the datasets of ImageNet and CIFAR-10. The obtained results show that Res-DNN leads to 2.5× lower energy for computations and an average of 30% overall energy reduction compared to those of the binary counterpart.
Software Implementation:
Modelsim
Xilinx
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Res-DNN: A Residue Number System-Based DNN Accelerator Unit
