深度学习加速器(卷积神经网络) 这是在 Verilog 中实现类似 MIT Eyeriss 的深度学习加速器 注:clacc代表卷积层加速器 RTL-Implementation-of-Two-Layer-CNN https://github.com/Haleski47/RTL-Implementation-of-Two-Layer-CNN https://github.com/Di5h3z/ECE-
到EDA (Electronic Design Automation)时代以Verilog和VHDL工具为主，再到现如今的ESL。
Figure 9 Open in figure viewerPowerPoint The overall hardware architecture of dynamic reconfigurable design of the coarse-to-fine (C2F) inference implementation. Each arrow indicates the direction of data flow, for example, from master to slave for AXI-based protocol and from the output of the...
The code is written by Verilog/SystemVerilog and Synthesized on Xilinx FPGA using Vivado. The code is just experimental for function, not full optimized. Architecture Only 4 elementary modules implemented: The conv, this module perform the convolution computing, the full connecting is also treated ...
摘要:现场可编程门阵列(FPGA)具有低功耗、高性能和灵活性的特点。FPGA神经网络加速的研究正在兴起,但大多数研究都基于国外的FPGA器件。为了改善国内FPGA的现状,提出了一种新型的卷积神经网络加速器,用于配备轻量级RISC-V软核的国产FPGA(紫光同创PG2L100H)。所提出的加速器的峰值性能达到153.6 GOP/s,仅占用14K LUT(查...
FPGA implementation ofCellular Neural Network(CNN) Initialization CNN CNN.vis Top-level design with initialization for A, B, I template SixteenbySixteen.javagenerates Verilog code for 16x16 layer modulesixteenbysixteen.v Default CornerDetection
We perform Verilog modeling and critical path optimization based on the AXI protocol standard. The accelerator is currently able to adapt to the computing requirements of the mainstream DCNN algorithm and at the same time can achieve a better energy efficiency ratio and computing efficiency. The ...
For Convolutional Neural Networks (CNNs), Depthwise Separable CNN (DSCNN) is the preferred architecture for Application Specific Integrated Circuit (ASIC) implementation on edge devices. It benefits from a multi-mode approximate multiplier proposed in this work. The proposed approximate multiplier uses ...
In our solution, we used dynamic precision weights for different layers and dynamic quantized activation precision to get the best combination between accuracy and speed. 2.3. Implementation Methodologies For most research, the CNN-based FPGA methodologies can be divided into two categories: register-...
The segmented memory interface is a better 'impedance match' to the PCIe hard core interface - data realignment can be done in the same clock cycle; no bursts, address decoding, arbitration, or reordering simplifies implementation and provides much higher performance than AXI. The architecture is ...