fpga深度学习加速代码
时间: 2023-10-10 15:14:48 浏览: 99
fpga学习用的代码
以下是一个基于FPGA的深度学习加速代码的示例:
```verilog
module fpga_dl_accelerator (
input clk, rst, enable,
input [31:0] input_data [0:3],
output [31:0] output_data [0:3]
);
// Define constants and parameters
parameter DATA_WIDTH = 32;
parameter NUM_INPUTS = 4;
parameter NUM_OUTPUTS = 4;
parameter NUM_LAYERS = 5;
parameter NUM_NEURONS = {784, 512, 256, 128, 10};
parameter NUM_WEIGHTS = {401408, 131072, 32768, 8192, 1280};
parameter NUM_BIASES = {512, 256, 128, 10};
parameter ACTIVATION_FUNCTION = 0; // 0 for ReLU, 1 for sigmoid
// Define internal signals
reg [DATA_WIDTH-1:0] input_buffer [0:NUM_INPUTS-1];
reg [DATA_WIDTH-1:0] output_buffer [0:NUM_OUTPUTS-1];
reg [DATA_WIDTH-1:0] weights [0:NUM_LAYERS-1][0:NUM_NEURONS-1][0:NUM_NEURONS-1];
reg [DATA_WIDTH-1:0] biases [0:NUM_LAYERS-1][0:NUM_NEURONS-1];
reg [DATA_WIDTH-1:0] neurons [0:NUM_LAYERS-1][0:NUM_NEURONS-1];
reg [DATA_WIDTH-1:0] gradients [0:NUM_LAYERS-1][0:NUM_NEURONS-1];
reg [DATA_WIDTH-1:0] deltas [0:NUM_LAYERS-1][0:NUM_NEURONS-1];
reg [DATA_WIDTH-1:0] errors [0:NUM_LAYERS-1][0:NUM_NEURONS-1];
// Define input and output ports
assign input_buffer[0] = input_data[0]; // Input layer
assign input_buffer[1] = output_buffer[0]; // Hidden layer 1
assign input_buffer[2] = output_buffer[1]; // Hidden layer 2
assign input_buffer[3] = output_buffer[2]; // Hidden layer 3
assign output_data[0] = output_buffer[1]; // Hidden layer 1
assign output_data[1] = output_buffer[2]; // Hidden layer 2
assign output_data[2] = output_buffer[3]; // Hidden layer 3
assign output_data[3] = output_buffer[4]; // Output layer
// Initialize weights and biases
initial begin
// Load weights and biases from memory
// ...
// Set initial values for neurons, gradients, deltas, and errors
for (int i = 0; i < NUM_LAYERS; i++) begin
for (int j = 0; j < NUM_NEURONS[i]; j++) begin
neurons[i][j] = 0;
gradients[i][j] = 0;
deltas[i][j] = 0;
errors[i][j] = 0;
end
end
end
// Define activation function
function [DATA_WIDTH-1:0] activation_function;
input [DATA_WIDTH-1:0] input_data;
case (ACTIVATION_FUNCTION)
0: begin // ReLU
if (input_data < 0) begin
activation_function = 0;
end else begin
activation_function = input_data;
end
end
1: begin // Sigmoid
activation_function = 1 / (1 + exp(-input_data));
end
default: begin // Default to ReLU
if (input_data < 0) begin
activation_function = 0;
end else begin
activation_function = input_data;
end
end
endcase
endfunction
// Define forward propagation
task forward_propagation;
input [DATA_WIDTH-1:0] input_data [0:NUM_NEURONS-1];
output [DATA_WIDTH-1:0] output_data [0:NUM_NEURONS-1];
input [DATA_WIDTH-1:0] weights [0:NUM_NEURONS-1][0:NUM_NEURONS-1];
input [DATA_WIDTH-1:0] biases [0:NUM_NEURONS-1];
input [DATA_WIDTH-1:0] activation_function;
begin
for (int i = 0; i < NUM_NEURONS; i++) begin
output_data[i] = biases[i];
for (int j = 0; j < NUM_NEURONS; j++) begin
output_data[i] += weights[i][j] * input_data[j];
end
output_data[i] = activation_function(output_data[i]);
end
end
endtask
// Define backward propagation
task backward_propagation;
input [DATA_WIDTH-1:0] input_data [0:NUM_NEURONS-1];
input [DATA_WIDTH-1:0] output_data [0:NUM_NEURONS-1];
input [DATA_WIDTH-1:0] weights [0:NUM_NEURONS-1][0:NUM_NEURONS-1];
input [DATA_WIDTH-1:0] activation_function;
output [DATA_WIDTH-1:0] gradients [0:NUM_NEURONS-1];
begin
for (int i = 0; i < NUM_NEURONS; i++) begin
gradients[i] = 0;
for (int j = 0; j < NUM_NEURONS; j++) begin
gradients[i] += weights[j][i] * input_data[j];
end
gradients[i] *= activation_function(output_data[i]);
end
end
endtask
// Define update weights and biases
task update_weights_biases;
input [DATA_WIDTH-1:0] input_data [0:NUM_NEURONS-1];
input [DATA_WIDTH-1:0] output_data [0:NUM_NEURONS-1];
input [DATA_WIDTH-1:0] weights [0:NUM_NEURONS-1][0:NUM_NEURONS-1];
input [DATA_WIDTH-1:0] biases [0:NUM_NEURONS-1];
input [DATA_WIDTH-1:0] learning_rate;
input [DATA_WIDTH-1:0] momentum;
output [DATA_WIDTH-1:0] new_weights [0:NUM_NEURONS-1][0:NUM_NEURONS-1];
output [DATA_WIDTH-1:0] new_biases [0:NUM_NEURONS-1];
begin
for (int i = 0; i < NUM_NEURONS; i++) begin
new_biases[i] = biases[i] - learning_rate * gradients[i];
for (int j = 0; j < NUM_NEURONS; j++) begin
new_weights[i][j] = weights[i][j] - learning_rate * input_data[j] * gradients[i] + momentum * (weights[i][j] - new_weights[i][j]);
end
end
end
endtask
// Define training loop
task train;
input [31:0] input_data [0:3];
input [31:0] expected_output_data;
input [31:0] learning_rate;
input [31:0] momentum;
begin
// Forward propagation
forward_propagation(input_buffer, neurons[0], weights[0], biases[0], activation_function);
for (int i = 1; i < NUM_LAYERS; i++) begin
forward_propagation(neurons[i-1], neurons[i], weights[i], biases[i], activation_function);
end
// Calculate errors and deltas in output layer
for (int i = 0; i < NUM_NEURONS[NUM_LAYERS-1]; i++) begin
errors[NUM_LAYERS-1][i] = expected_output_data - neurons[NUM_LAYERS-1][i];
deltas[NUM_LAYERS-1][i] = errors[NUM_LAYERS-1][i] * activation_function(neurons[NUM_LAYERS-1][i], true);
end
// Backward propagation
for (int i = NUM_LAYERS-2; i >= 0; i--) begin
backward_propagation(neurons[i+1], neurons[i], weights[i+1], activation_function, gradients[i]);
for (int j = 0; j < NUM_NEURONS[i]; j++) begin
deltas[i][j] = gradients[i][j] * activation_function(neurons[i][j], true);
end
end
// Update weights and biases
update_weights_biases(input_buffer, neurons[0], weights[0], biases[0], learning_rate, momentum, weights[0], biases[0]);
for (int i = 1; i < NUM_LAYERS; i++) begin
update_weights_biases(neurons[i-1], neurons[i], weights[i], biases[i], learning_rate, momentum, weights[i], biases[i]);
end
end
endtask
// Define testing loop
task test;
input [31:0] input_data [0:3];
output [31:0] output_data;
begin
// Forward propagation
forward_propagation(input_buffer, neurons[0], weights[0], biases[0], activation_function);
for (int i = 1; i < NUM_LAYERS; i++) begin
forward_propagation(neurons[i-1], neurons[i], weights[i], biases[i], activation_function);
end
// Output result
output_data = neurons[NUM_LAYERS-1][0];
end
endtask
endmodule
```
注:以上代码仅供参考,具体实现可能因应用场景、FPGA型号、性能需求等因素而有所不同。
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