CQNN: a CGRA-based QNN Framework

Quantized Neural Networks (QNNs) have drawn tremendous attention since - when compared with Convolution Neural Networks (CNNs) - they often dramatically reduce computation, communication, and storage demands with negligible loss in accuracy. To find an optimal balance between performance and accuracy, developers use different data-widths for different layers and channels. Given this large parameter space, it is challenging to design a QNN accelerator which is generally efficient for various and flexible model configurations. In this paper we propose CQNN, a novel Coarse-Grained Reconfigurable Architecture-based (CGRA) QNN acceleration framework. CQNN has a large number of basic components for binary functions. By programming CQNN at runtime according to the target QNN model, these basic components are integrated to support QNN functions with any data-width and hyperparameter requirements. The result is an optimal QNN for the target model. The framework includes compiler, hardware design, simulator, and RTL generator. Experimental results show CQNNs can complete the inference of AlexNet and VGG-16 within 0.13ms and 2.63ms, respectively. We demonstrate the design on an FPGA platform; however, this is only for showcasing the method: the approach does not rely on any FPGA-specific features and can thus be implemented as ASIC as well.