Artificial Neural Network Implementation in FPGA Using Multiplexer-Based Weight Updating for Efficient Resource UtilizationRavi KumarDeepak Gupta
and degradation factors are important. This work discusses and compares emerging technologies and hardware for NN implementations, focusing on the requirements for wearable biomedical devices. To address these challenges, field-programmable gate array (FPGA) implementation is adopted as a preferred solution...
This paper presents a hardware implementation of multilayer feedforward neural networks (NN) using reconfigurable field-programmable gate arrays (FPGAs). Despite improvements in FPGA densities, the numerous multipliers in an NN limit the size of the network that can be implemented using a single FPGA...
In this paper we present a hardware implementation of Long-Short Term Memory (LSTM) recurrent network on the programmable logic Zynq 7020 FPGA from Xilinx. We implemented a RNN with 2 layers and 128 hidden units in hardware and it has been tested using a character level language model. The ...
select article An Optimized Multi-layer Spiking Neural Network implementation in FPGA Without Multipliers Research articleOpen access An Optimized Multi-layer Spiking Neural Network implementation in FPGA Without Multipliers Ali Mehrabi, Yeshwanth Bethi, André van Schaik, Saeed Afshar ...
题目:Going Deeper with Embedded FPGA Platform for Convolutional Neural Network 日期&会议:Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays(FPGA) 文章主要做的事情: 在嵌入式FPGA平台上加速CNNs,为Image-Net大型图像分类开发了CNN加速器。
A neural network accelerated optimization method for FPGA hardware platform is proposed. The method realizes the optimized deployment of neural network alg
To explore a reasonable implementation of the spiking neural networks, a novel method is proposed in which the network topology is simulated by the software simulation libraries, and the key computations are handed over to the FPGA forparallel computing to meet the requirements of easydevelopment, ...
Therefore, the implementation of CNN-based target detection algorithm accelerator using FPGA has become a hot research topic. For example, in 2016, Chen et al. designed a SIMD convolutional neural network acceleration system based on FPGA, which reduced the gap between the theoretical and actual ...
In this work, Convolutional Neural Network (CNN) is applied for defect identification of Swiven Cap (one type of medical component) based on Field-Programmable Gate Array (FPGA) implementation. Caffe is used as the platform to develop the CNN model. After training phase, a confusion matrix is...