以下是一个简化的Radix-2 FFT的Verilog实现示例:
module FFT (
input wire clk,
input wire rst,
input wire signed [15:0] x_real [0:15], // 输入实部
input wire signed [15:0] x_imag [0:15], // 输入虚部
output reg signed [15:0] y_real [0:15], // 输出实部
output reg signed [15:0] y_imag [0:15] // 输出虚部
);
parameter N = 16; // FFT大小,必须是2的幂次方
// FFT模块内部信号
reg signed [15:0] twiddle_real [0:N/2-1];
reg signed [15:0] twiddle_imag [0:N/2-1];
reg signed [15:0] stage_real [0:N-1];
reg signed [15:0] stage_imag [0:N-1];
reg signed [15:0] butterfly_real [0:N-1];
reg signed [15:0] butterfly_imag [0:N-1];
// 初始化蝶形运算所需的旋转因子
initial begin
for (int i = 0; i < N/2; i = i + 1) begin
twiddle_real[i] = cos(2 * $pi * i / N);
twiddle_imag[i] = -sin(2 * $pi * i / N);
end
end
// FFT计算
always @(posedge clk or posedge rst) begin
if (rst) begin
// 复位时初始化状态
for (int i = 0; i < N; i = i + 1) begin
stage_real[i] <= 16'h0000;
stage_imag[i] <= 16'h0000;
butterfly_real[i] <= 16'h0000;
butterfly_imag[i] <= 16'h0000;
y_real[i] <= 16'h0000;
y_imag[i] <= 16'h0000;
end
end else begin
// 输入数据赋值到第一级蝶形运算
stage_real[0] <= x_real[0];
stage_imag[0] <= x_imag[0];
// FFT计算的蝶形运算
for (int stage = 1; stage < $clog2(N); stage = stage + 1) begin
// 计算每个蝶形运算的输入
for (int i = 0; i < N; i = i + 1) begin
butterfly_real[i] = stage_real[i] + (stage_imag[i] * twiddle_real[i >> stage] - stage_imag[i] * twiddle_imag[i >> stage]);
butterfly_imag[i] = stage_imag[i] + (stage_imag[i] * twiddle_real[i >> stage] + stage_real[i] * twiddle_imag[i >> stage]);
end
// 更新下一级蝶形运算的输入
for (int i = 0; i < N; i = i + 1) begin
stage_real[i] = butterfly_real[i];
stage_imag[i] = butterfly_imag[i];
end
end
// 输出结果
for (int i = 0; i < N; i = i + 1) begin
y_real[i] = butterfly_real[i];
y_imag[i] = butterfly_imag[i];
end
end
end
endmodule
这个例子中,FFT模块接收实部和虚部的输入,产生对应的实部和虚部的输出。模块中的蝶形运算通过循环计算每个阶段的输入和输出,并使用旋转因子进行计算。需要注意的是,这只是一个简化的FFT实现,实际中可能需要更复杂的设计来处理各种情况,并可能需要添加流水线和优化以提高性能。
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