Reconfigurable CNN Accelerator Embedded in Instruction Extended RISC-V Core

The convolution neural network (CNN) is widely used in many aspects, such as Speech Recognition, Face Detection, and Image Classification. Utilizing a GPU is the traditional way of implementing CNN, which is fast but inefficient. In pursuit of lower power consumption and higher efficiency, we prefer application-specific hardware computing. This paper proposes a run-time reconfigurable CNN accelerator SoC (CNN-AS) architecture embedded in instruction-extended RISC-V. The application-specific extension instruction set is designed to accelerate high-frequency operations in CNN. To optimize the circuit structure, we created an 8-bit dynamic fixed-point (DFP) scheme within the CNN-AS. The accuracy of the DFP implementation is also compared with the TensorFlow float implementation. Furthermore, the corresponding software of RESNET and VGG16 is described and simulated with CNN-AS. Lastly, we compare the overall simulated results with other non-SoC FPGA designs in efficiency, throughput, and power.