sigmoid函数导数的硬件实现(verilog)

仿真思路

其实是使用了很普通的硬件的函数的仿真方法，但是有趣的是我为了提升精度将划分划分的更加细致了；
看一下matlab源代码还有图像大家就知道了：

function Mysolution()
x1=0:0.01:0.125;
f1=0.2498 ;
x2=0.125:0.01:0.25;
f2=0.2478;
x3=0.25:0.01:0.375;
f3= 0.2440 ;
x4=0.375:0.01:0.5;
f4=0.2384;
x5=0.5:0.01:0.625;
f5=0.2312;
x6=0.625:0.01:0.75;
f6=0.2226;
x7=0.75:0.01:0.875;
f7=0.2129;
x8=0.875:0.01:1;
f8=0.2022;
x9=1:0.01:1.125;
f9=0.1909;
x10=1.125:0.01:1.25;
f10=0.1791;
x11=1.25:0.01:1.375;
f11=0.1671;
x12=1.375:0.01:1.5;
f12=0.1551;
x13=1.5:0.01:1.625;
f13=0.1443;
x14=1.625:0.01:1.75;
f14=0.1317;
x15=1.75:0.01:1.875;
f15=0.1206;
x16=1.875:0.01:2;
f16=0.1101;
x17=2:0.01:2.125;
f17=0.1001;
x18=2.125:0.01:2.25;
f18=0.0907;
x19=2.25:0.01:2.375;
f19=0.0820;
x20=2.375:0.01:2.5;
f20=0.0739;
x21=2.5:0.01:2.625;
f21=0.0665;
x22=2.625:0.01:2.75;
f22=0.0597;
x23=2.75:0.01:2.875;
f23=0.0534;
x24=2.875:0.01:3;
f24=0.0478;
x25=3:0.01:3.125;
f25=0.0427;
x26=3.125:0.01:3.25;
f26=0.0381;
x27=3.25:0.01:3.375;
f27=0.0339;
x28=3.375:0.01:3.5;
f28=0.0302;
x29=3.5:0.01:3.625;
f29=0.0268;
x30=3.625:0.01:3.75;
f30=0.0238;
x31=3.75:0.01:3.875;
f31=0.0211;
x32=3.875:0.01:4;
f32=0.0188;
x33=4:0.01:4.125;
f33=0.0166;
x34=4.125:0.01:4.25;
f34=0.0147;
x35=4.25:0.01:4.375;
f35=0.0130;
x36=4.375:0.01:4.5;
f36=0.0116;
x37=4.5:0.01:4.625;
f37=0.0102;
x38=4.625:0.01:4.75;
f38=0.009;
x39=4.75:0.01:4.875;
f39=0.008;
x40=4.875:0.01:5;
f40=0.0071;
x41=5:0.01:5.125;
f41=0.0063;
x42=5.125:0.01:5.25;
f42=0.0055;
x43=5.25:0.01:5.375;
f43=0.0049;
x44=5.375:0.01:5.5;
f44=0.0043;
x45=5.5:0.01:5.625;
f45=0.0038;
x46=5.625:0.01:5.75;
f46=0.0034;
x47=5.75:0.01:5.875;
f47=0.0030;
x48=5.875:0.01:6;
f48=0.0026;
x49=6:0.01:6.125;
f49=0.0023;
x50=6.125:0.01:6.25;
f50=0.0020;
x51=6.25:0.01:6.375;
f51=0.0018;
x52=6.375:0.01:6.5;
f52=0.0016;
x53=6.5:0.01:6.625;
f53=0.0014;
x54=6.625:0.01:6.75;
f54=0.0012;
x55=6.75:0.01:6.875;
f55=0.0011;
x56=6.875:0.01:7;
f56=0.0010;
x57=7:0.01:7.125;
f57=0.0009;
x58=7.125:0.01:7.25;
f58=0.0008;
x59=7.25:0.01:7.375;
f59=0.0007;
x60=7.375:0.01:7.5;
f60=0.0006;
x61=7.5:0.01:7.625;
f61=0.0005;
x62=7.625:0.01:7.75;
f62=0.0005;
x63=7.75:0.01:7.875;
f63=0.0004;
x64=7.875:0.01:8;
f64=0.0004;
x=0.0625:0.125:7.9375;
fx=exp(-x)./(1+exp(-x)).^2;
plot(x,fx,'b');
hold on;
plot(x1,f1,'r');hold on;
plot(x2,f2,'r');hold on;
plot(x3,f3,'r');hold on;
plot(x4,f4,'r');hold on;
plot(x5,f5,'r');hold on;
plot(x6,f6,'r');hold on;
plot(x7,f7,'r');hold on;
plot(x8,f8,'r');hold on;
plot(x9,f9,'r');hold on;
plot(x10,f10,'r');hold on;
plot(x11,f11,'r');hold on;
plot(x12,f12,'r');hold on;
plot(x13,f13,'r');hold on;
plot(x14,f14,'r');hold on;
plot(x15,f15,'r');hold on;
plot(x16,f16,'r');hold on;
plot(x17,f17,'r');hold on;
plot(x18,f18,'r');hold on;
plot(x19,f19,'r');hold on;
plot(x20,f20,'r');hold on;
plot(x21,f21,'r');hold on;
plot(x22,f22,'r');hold on;
plot(x23,f23,'r');hold on;
plot(x24,f24,'r');hold on;
plot(x25,f25,'r');hold on;
plot(x26,f26,'r');hold on;
plot(x27,f27,'r');hold on;
plot(x28,f28,'r');hold on;
plot(x29,f29,'r');hold on;
plot(x30,f30,'r');hold on;
plot(x31,f31,'r');hold on;
plot(x32,f32,'r');hold on;
plot(x33,f33,'r');hold on;
plot(x34,f34,'r');hold on;
plot(x35,f35,'r');hold on;
plot(x36,f36,'r');hold on;
plot(x37,f37,'r');hold on;
plot(x38,f38,'r');hold on;
plot(x39,f39,'r');hold on;
plot(x40,f40,'r');hold on;
plot(x41,f41,'r');hold on;
plot(x42,f42,'r');hold on;
plot(x43,f43,'r');hold on;
plot(x44,f44,'r');hold on;
plot(x45,f45,'r');hold on;
plot(x46,f46,'r');hold on;
plot(x47,f47,'r');hold on;
plot(x48,f48,'r');hold on;
plot(x49,f49,'r');hold on;
plot(x50,f50,'r');hold on;
plot(x51,f51,'r');hold on;
plot(x52,f52,'r');hold on;
plot(x53,f53,'r');hold on;
plot(x54,f54,'r');hold on;
plot(x55,f55,'r');hold on;
plot(x56,f56,'r');hold on;
plot(x57,f57,'r');hold on;
plot(x58,f58,'r');hold on;
plot(x59,f59,'r');hold on;
plot(x60,f60,'r');hold on;
plot(x61,f61,'r');hold on;
plot(x62,f62,'r');hold on;
plot(x63,f63,'r');hold on;
plot(x64,f64,'r');hold on;
xx=0.0625:0.125:8-0.0625;
ff=exp(-xx)./(1+exp(-xx)).^2
plot(xx,ff,'black');
end

确实比较简单，但是硬件实现遇见了很大的麻烦，为什么呢，因为我们用到的数据是16位的浮点型，大概画一下结构好了；
；
也就是说，我现在知道了每个范围的x所对应的y的值，在输入一个十六位二进制的浮点型数据之后，根据这个数据得知rom里面对应的y的地址值，反正就是很麻烦我也没啥时间写，直接上代码吧。
先做一个y值得rom

memory_initialization_radix=2;
memory_initialization_vector=
0011000011111111
0011000011111101
0011000011111001
0011000011110100
0011000011101100
0011000011100011
0011000011011001
0011000011001111
0011000011000011
0011000010110111
0011000010101011
0011000010011110
0011000010010010
0011000010000110
0010110001111011
0010110001110000
0010110001100110
0010110001011100
0010110001010011
0010110001001011
0010110001000100
0010100000111101
0010100000110110
0010100000110000
0010100000101011
0010100000100110
0010100000100010
0010010000011110
0010010000011011
0010010000011000
0010010000010101
0010010000010011
0010010000010001
0010000000001111
0010000000001101
0010000000001011
0010000000001010
0010000000001001
0010000000001000
0001110000000111
0001110000000110
0001110000000101
0001110000000100
0001110000000100
0001100000000011
0001100000000011
0001100000000011
0001100000000010
0001100000000010
0001100000000010
0001010000000001
0001010000000001
0001010000000001
0001010000000001
0001010000000001
0001000000000000
0001000000000000
0001000000000000
0001000000000000
0001000000000000
0001000000000000
0000110000000000
0000110000000000
0000110000000000;

然后用x对应到地址，我设计的很巧妙的呦：

//由x得到adress
module Mysolution(clk,read_x,addra);
input clk;
input [15:0] read_x;
output [5:0] addra;
reg [15:0] Data1;
reg [15:0] Data2;
reg [5:0] addra;
always @(posedge clk)
begin
Data1={6'b000001,read_x[9:0]};
Data2={6'b000000,read_x[9:0]};
if(read_x[14:10]>=5'b01111)
addra=Data1[12:7];
else
addra=Data2[12:7];
end
endmodule

module addrtodata(clk,ena,read_x,douta);
input clk,ena;
input[15:0] read_x;
output [15:0] douta;
wire [15:0] douta;
wire [5:0] addra;
Mysolution  mysolution_init_0(.clk(clk),.read_x(read_x),.addra(addra));
blk_mem_gen_0 addrtodata(.clka(clk),.addra(addra),.douta(douta),.ena(ena));
endmodule

好了，testbench自己写，加油；

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sigmoid函数导数的硬件实现(verilog)

仿真思路

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