```verilog module convolution_accelerator ( input wire clk, input wire reset, input wire [7:0] input_image [0:27][0:27], input wire [7:0] kernel [0:2][0:2], output wire [15:0] conv_output [0:26][0:26] ); reg [15:0] sum; integer i, j, m, n; always @(posedge cl...
论文 [6](Calculation Optimization for Convolutional Neural Networks and FPGA-based Accelerator Design Using the Parameters Sparsity)就进行了优化,以通过有效的数据多路复用来减少流量。 4.2 使用硬件模板设计通用加速器框架 论文[16](FP-DNN: An Automated Framework for Mapping Deep Neural Networks onto FPGAs ...
• U. Aydonat, S. O'Connell, D. Capalija, A. C. Ling, and G. R. Chiu. "An OpenCL™ Deep Learning Accelerator on Arria 10," in Proc. FPGA 2017. • N. Suda, V. Chandra, G. Dasika, A. Mohanty, Y. F. Ma, S. Vrudhula, J. S. Seo, and Y. Cao, "Throughput-Opt...
在最近的 International Symposium onField Programmable Gate Arrays (ISFPGA) 上,Intel Accelerator Architecture Lab (AAL) 的 Eriko Nurvitadhi 博士提出了一篇名为 Can FPGAs beat GPUs in AcceleratingNext-Generation Deep Neural Networks 的论文。他们的研究以最新的高性能的 NVIDIA Titan X Pascal * Graphics ...
Fig. 1: Development history of the nerural network accelerator based on FPGA. II. BACKGROUND Deep learning combines low-level features to form more abstract high-level representation attribute categories or fea- tures to discover distributed feature representations of data.Its concept was proposed by...
随着高带宽内存(HBM)的发展,FPGA正变得越来越强大,HBM 给了FPGA 更多能力去缓解再一些应用中遇到的内存带宽瓶颈和处理更多样的应用。然而,HBM 的性能表现我们了解地还不是特别精准,尤其是在 FPGA 平台上。这篇文章我们将会在HBM 的说明书和它的实际表现之间建立起桥梁
Title:FPGA, the mainstream accelerator of choice for the FinTech Industry Abstract:The increase in the requirement for greater levels of compute density driven by increasing regulatory pressure is critically driving the need for acceleration in the financial data-centers. For a many year’s, CPU’...
deep-learningbutterflyfpga-accelerator UpdatedMay 21, 2023 Verilog Squeezenet V1.1 on Cyclone V SoC-FPGA at 450ms/image, 20x faster than ARM A9 processor alone. A project for 2017 Innovate FPGA design contest. openclfpga-acceleratorcnn-classification ...
北大的CEEC也看过相应论文：Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural ...
We further demonstrate the efficacy of multiplier-free quantization using a state-of-the-art binarized neural network accelerator designed for an embedded FPGA (AMD Xilinx Ultra96 v2). We plan to release QFX in open-source format. PDF Abstract ...