Flare: An FPGA-Based Full Precision Low Power CNN Accelerator with Reconfigurable Structure

Abstract

Convolutional neural networks (CNNs) have significantly advanced various fields; however, their computational demands and power consumption have escalated, posing challenges for deployment in low-power scenarios. To address this issue and facilitate the application of CNNs in power constrained environments, the development of dedicated CNN accelerators is crucial. Prior research has predominantly concentrated on developing low precision CNN accelerators using code generated from high-level synthesis (HLS) tools. Unfortunately, these approaches often fail to efficiently utilize the computational resources of field-programmable gate arrays (FPGAs) and do not extend well to full precision scenarios. To overcome these limitations, we integrate vector dot products to unify the convolution and fully connected layers. By treating the row vector of input feature maps as the fundamental processing unit, we balance processing latency and resource consumption while eliminating data rearrangement time. Furthermore, an accurate design space exploration (DSE) model is established to identify the optimal design points for each CNN layer, and dynamic partial reconfiguration is employed to maximize each layer’s access to computational resources. Our approach is validated through the implementation of AlexNet and VGG16 on 7A100T and ZU15EG platforms, respectively. We achieve an average convolutional layer throughput of 28.985 GOP/s and 246.711 GOP/s for full precision. Notably, the proposed accelerator demonstrates remarkable power efficiency, with a maximum improvement of 23.989 and 15.376 times compared to current state-of-the-art FPGA implementations.