Convolutional neural networks (CNN) are widely used in resource-constrained devices in IoT applications. In order to reduce the computational complexity and memory footprint, the resource-constrained devices use fixed-point representation. This representation consumes less area and energy in hardware with similar classification accuracy compared to the floating-point ones. However, to employ the low-precision fixed-point representation, various considerations to gain high accuracy are required. Although many quantization and re-training techniques are proposed to improve the inference accuracy, these approaches are time-consuming and require access to the entire dataset. This paper investigates the effect of different fixed-point hardware units on CNN inference accuracy. To this end, we provide a framework called Fixflow to evaluate the effect of fixed-point computations performed at hardware level on CNN classification accuracy. We can employ different fixed-point considerations at the hardware accelerators.This includes rounding methods and adjusting the precision of the fixed-point operation's result. Fixflow can determine the impact of employing different arithmetic units (such as truncated multipliers) on CNN classification accuracy. Moreover, we evaluate the energy and area consumption of these units in hardware accelerators. We perform experiments on two common MNIST and CIFAR-10 datasets. Our results show that employing different methods at the hardware level specially with low-precision, can significantly change the classification accuracy.
Automatic Generation of Multi-precision Multi-arithmetic CNN Accelerators for FPGAs
University of Cambridge（剑桥大学）；Shenzhen Institutes of Advanced Technology（深圳先进技术研究院）；Microsoft Research Cambridge（微软剑桥研究院）；Imperial College London（帝国理工学院）；University of Macau（澳门大学）
Trinity College Dublin, The University of Dublin（都柏林大学圣三一学院）；The School of Computer Science and Statistics（计算机科学与统计学院）；Institute of Technology Carlow（卡洛理工学院）
Continuous-Flow Data-Rate-Aware CNN Inference on FPGA
Department of Applied Computer Science, Fulda University of Applied Sciences（应用计算机科学系，弗劳达应用科学大学）；Department of Electronic Engineering, ETSI de Telecomunicación, Universidad Politécnica de Madrid（电子工程系，马德里理工大学电信学院）；Department of Electrical Engineering, Linköping University（电气工程系，林雪平大学）
