openclfpga-acceleratorcnn-classification UpdatedJun 27, 2018 Objective-C BARVINN: A Barrel RISC-V Neural Network Accelerator:https://barvinn.readthedocs.io/en/latest/ machine-learningdeep-neural-networksquantizationrisc-vfpga-accelerator UpdatedJan 5, 2025 ...
CNN accelerator implemented with Spinal HDL fpgacnnyoloxilinxobject-detectionspinalhdl UpdatedJan 29, 2024 Scala Antmicro's fast, vendor-neutral DMA IP in Chisel fpgadma UpdatedNov 28, 2024 Scala carlosedp/chiselv Sponsor Star102 A RISC-V Core (RV32I) written in Chisel HDL ...
https://github.com/Haleski47/RTL-Implementation-of-Two-Layer-CNN https://github.com/Di5h3z/ECE-564-Convolutional-Neural-Network-Accelerator 具有详细设计的两层 CNN 详细的设计文档: https://github.com/Haleski47/RTL-Implementation-of-Two-Layer-CNN/blob/master/report/Apar%20Bansal%20ECE564%20Pro...
！话不多说先贴项目代码的地址：https://github.com/WalkerLau/Accelerating-CNN-with-FPGAgithub.c...
network accelerator using HLS for a System on Chip design[J]. arXiv, 2020.主要贡献1.通用的CNN...
DNN-Hardware-Accelerator https://github.com/ryaanluke/DNN-Hardware-Accelerator https://github.com/gwatcha/dnn_accelerator 介绍 在本实验中,将以嵌入式 Nios II 系统为核心构建深度神经网络加速器。在本项目中还将学习如何与片外 SDRAM 连接,以及如何使用 PLL 生成具有特定属性的时钟。
https:///thedatabusdotio/fpga-ml-accelerator 1. 参考链接 [1] https://cs231n.github.io/convolutional-networks/#conv [2] https://ieeexplore.ieee.org/document/517077 [3]https://www.freesion.com/article/5936434427/ [4]https://mp.weixin.qq.com/s?__biz=MzIwNDY0MjYzOA==&mid=2247501777...
在数据的片外传输上,本系统采用的accelerator interface是streaming interface,该interface要求数据的实际传输量必须等于预期传输量。由于每层卷积的结构不同,所以每层实际的数据传输量也不一样,但我们所有层都共用一个accelerator interface,这似乎无法满足streaming interface的要求,造成传输界面阻塞,最终下板的时候会使程序...
CNN-Accelerator-VLSI https://github.com/lirui-shanghaitech/CNN-Accelerator-VLSI 详细要求在./resource/Project_2.0.pdf中有详细介绍。 下面列出一些主要要求: 所有输入特征图和权重的位长为 8 位,输出为 25 位数据。 输入特征图带宽为8x8位,权重带宽也是8x8位(最大同时读取8个输入特征图和8个权重) ...
To address the computational demands of hybrid models, we introduce a hardware accelerator architecture that enables parallel processing of both CNN and LSTM sub-models. Employing eight processing elements per layer and optimizing memory usage through array partitioning, unrolling, and memory reuse, our...