… In this paper, we propose a fully binarized neural network accelerator. With the hardware-oriented
algorithm optimizations, we binarize the convolution across all the layers and …

… Gupta, “Binarized convolutional neural networks with separable filters for efficient … , “Accelerating
binarized convolutional neural networks with software-programmable fpgas,” in FPGA. …

… FPGA by up to 3x in comparison with a static design that deploys the same accelerator cores
on a software-programmable FPGA … devices when inter-FPGA communication is reduced. …

… and the NVIDIA GeForce RTX 2080 GPU, our BNN hardware module on the FPGA can
accelerate the frames per second (FPS) by a factor of 3,690.18 and a factor of 1.07, respectively. …

… This paper presents a binary convolutional neural network acceleration system with a
hardware/software co-design framework. An improved binary network training approach with …

… computations on very deep neural networks whose parameters would … FPGAbased computing
systems, focusing on the implementation of a binarized deep convolutional neural network …

… binarized neural network synthesizer) is an open-source tool flow for a binarized deep neural
network toward FPGA … on the GPU and inference on the FPGA. Since all the operation is …

… To the best of our knowledge, Cifar-10 AlexNet is the first work to accelerate binarized
AlexNet model on Cifar10, and thus the performance results of this model on FCABNN are …

… On-chip memory based binarized convolutional deep neural network applying batch
normalization free technique on an FPGA. In Proceedings of the 2017 IEEE International Parallel …

… accelerate computing tasks with HLS, such as [1] and [2]. In this paper we investigate the FPGA
acceleration of the Convolutional Neural Networks (… with Software-Programmable FPGAs,…