(* equivalent_register_removal = "no" *)在verilog中是什么意思

在 Verilog HDL 中，`equivalent_register_removal` 是一种可选的优化指令。当该指令设置为 `"no"` 时，它告诉综合工具不要对逻辑电路中的等效寄存器进行删除优化。这意味着，即使逻辑电路中存在多个等效寄存器，它们也将保留在合成后的电路中，而不会被删除。

通常，综合工具会尝试在逻辑电路中删除冗余的等效寄存器，以减少电路的面积和功耗。但在某些情况下，这种优化可能会导致逻辑电路的功能被改变。在这种情况下，可以通过设置 `equivalent_register_removal` 为 `"no"` 来禁用此优化。

相关问题

def crop_pointcloud(data_crop, x_o, y_o, x_i, y_i, R_o, R_i, z_critical): K_o = R_o ** 2 / range_z K_i = R_i ** 2 / range_z for z in range(range_z): r_o = np.sqrt(z * K_o) data_layer = data_crop[:, :, z] d_o = np.sqrt(x_o ** 2 + y_o ** 2) d_i = np.sqrt(x_i ** 2 + y_i ** 2) if z < z_critical: r_i = 0 else: r_i = np.sqrt(z * K_i) data_crop[:, :, z] = np.where((d_o > r_o) | (d_i <= r_i), 0, data_layer) return data_crop c++ Eigen

The code you provided is written in Python and uses NumPy library for array manipulation. It crops a 3D point cloud data based on the given parameters and returns the cropped data.

Here's the equivalent C++ code using Eigen library for array manipulation:

#include <Eigen/Dense>

Eigen::Tensor<float, 3> crop_pointcloud(Eigen::Tensor<float, 3> data_crop, float x_o, float y_o, float x_i, float y_i, float R_o, float R_i, int z_critical) {
    int range_z = data_crop.dimension(2);
    float K_o = R_o * R_o / range_z;
    float K_i = R_i * R_i / range_z;
    
    for (int z=0; z<range_z; z++) {
        float r_o = sqrt(z * K_o);
        Eigen::Tensor<float, 2> data_layer = data_crop.chip(z, 2);
        float d_o = sqrt(x_o * x_o + y_o * y_o);
        float d_i = sqrt(x_i * x_i + y_i * y_i);
        
        float r_i = (z < z_critical) ? 0 : sqrt(z * K_i);
        
        data_crop.chip(z, 2) = (d_o > r_o || d_i <= r_i).select(0, data_layer);
    }
    
    return data_crop;
}

Note: This code assumes that the input data is a 3D tensor of shape (N, M, K), where N, M, and K are the number of points in x, y, and z dimensions, respectively.

def crop_pointcloud(data_crop, x_o, y_o, x_i, y_i, R_o, R_i, z_critical): range_z = 400; K_o = R_o ** 2 / range_z K_i = R_i ** 2 / range_z for z in range(range_z): r_o = np.sqrt(z * K_o) data_layer = data_crop[:, :, z] d_o = np.sqrt(x_o ** 2 + y_o ** 2) d_i = np.sqrt(x_i ** 2 + y_i ** 2) if z < z_critical: r_i = 0 else: r_i = np.sqrt(z * K_i) data_crop[:, :, z] = np.where((d_o > r_o) | (d_i <= r_i), 0, data_layer) return data_crop 转为c++ Eigen，Tensor

Here's the equivalent code in C++ using the Eigen and Tensor libraries:

#include <Eigen/Dense>
#include <unsupported/Eigen/CXX11/Tensor>

Eigen::Tensor<double, 3> crop_pointcloud(Eigen::Tensor<double, 3> data_crop, double x_o, double y_o, double x_i, double y_i, double R_o, double R_i, int z_critical) {
    int range_z = 400;
    double K_o = pow(R_o, 2) / range_z;
    double K_i = pow(R_i, 2) / range_z;
    for (int z = 0; z < range_z; z++) {
        double r_o = sqrt(z * K_o);
        Eigen::Tensor<double, 2> data_layer = data_crop.chip(z, 2);
        double d_o = sqrt(pow(x_o, 2) + pow(y_o, 2));
        double d_i = sqrt(pow(x_i, 2) + pow(y_i, 2));
        double r_i = z < z_critical ? 0 : sqrt(z * K_i);
        data_crop.chip(z, 2) = (d_o > r_o || d_i <= r_i).select(Eigen::Tensor<double, 2>::Zero(data_layer.rows(), data_layer.cols()), data_layer);
    }
    return data_crop;
}

Note that I assumed the input `data_crop` is a 3D tensor with dimensions (rows, cols, depth), and I used the `chip` method to extract each 2D layer of the tensor as a 2D Eigen tensor. Also, I used the `select` method to apply the cropping condition on each element of the tensor and set it to zero if the condition is true.