fpga_api_on_cpu.cpp

#include"fpga_api.h"
#include<stdio.h>
#include<iostream>
#include<cstring>
#include<unistd.h>

using namespace std;

#define min(x,y) (((x)<(y))?(x):(y))

FPGA::FPGA(off_t data_addr, off_t output_addr, int m_size, int v_size)
{
	m_size_ = m_size;
	v_size_ = v_size;

	m1_size_ = v_size * v_size;
	m2_size_ = v_size * v_size;
	data_size_ = (m_size_+1)*v_size_; // fpga bram data size

	data_size_M = (v_size_+v_size_)*v_size_; // for Matrix matrix multiplication

	output_ = new unsigned int[m_size_];    // use output_ as tempolar output
	output_M = new unsigned int[v_size_*v_size_];

	data_ = new float[data_size_];	
	data_M = new float[data_size_M]; // for Matrix matrix multiplication

	num_block_call_ = 0;
}
FPGA::~FPGA()
{
	// delete[] output_;
	delete[] output_M;
	// delete[] data_;
	delete[] data_M;
}

float* FPGA::matrix(void)
{
	return data_ + v_size_;
}

float* FPGA::vector(void)
{
	return data_;
}

float* FPGA::matrix_M1(void)
{
	return data_M;
}

float* FPGA::matrix_M2(void)
{
	return data_M + m1_size_;
}

void FPGA::reset(void)
{
	num_block_call_ = 0;
}

int FPGA::num_block_call(void)
{
	return num_block_call_;
}

const float* FPGA::blockMM()
{
	num_block_call_ += 1;

	// cpu version
	float* m1 = this->matrix_M1();
	float* m2 = this->matrix_M2();
	float* out  = reinterpret_cast<float*>(output_M);  

	for(int i = 0; i < v_size_; ++i)
	{
		for(int j = 0; j < v_size_; ++j){    
			out[v_size_*i+j] = 0;
			for(int k = 0; k < v_size_; ++k){
				out[v_size_*i+j] += m1[v_size_*i+k] * m2[v_size_*k + j];
			}
		}
	}

	for(int i = 0; i < m1_size_; ++i)
		data_M[i] = out[i];

	return data_M;    
}

const float* FPGA::blockMV()
{
	num_block_call_ += 1;

	// cpu version
	float* vec = this->vector();
	float* mat = this->matrix();
	float* out  = reinterpret_cast<float*>(output_);  

	for(int i = 0; i < m_size_; ++i)
	{
		out[i] = 0;
		for(int j = 0; j < v_size_; ++j)
			out[i] += vec[j] * mat[v_size_*i + j];
	}

	for(int i = 0; i < m_size_; ++i)
		data_[i] = out[i];

	return data_;    
}

void FPGA::largeMV(const float* large_mat, const float* input, float* output, int num_input, int num_output)
{
	float* vec = this->vector();
	float* mat = this->matrix();

	// 0) Initialize output vector
	for(int i = 0; i < num_output; ++i) output[i] = 0;

	for(int i = 0; i < num_output; i += m_size_) {
		for(int j = 0; j < num_input; j += v_size_) {
			// 0) Initialize input vector
			int block_row = min(m_size_, num_output-i);
			int block_col = min(v_size_, num_input-j);

			// !) Assign a vector
			/* IMPLEMENT */
			for (int k=0;k<block_col;k++) vec[k]=input[j+k];
			for (int k=block_col;k<v_size_;k++) vec[k]=0;

			// 2) Assign a matrix
			/* IMPLEMENT */
			for (int k1=0;k1<block_row;k1++) {
							for (int k2=0;k2<block_col;k2++)
											mat[v_size_*k1 + k2] = large_mat[num_input*(i+k1) + j+k2];
							for (int k2=block_col;k2<v_size_;k2++) mat[v_size_*k1 + k2] = 0;
			}
			for (int k1=block_row;k1<m_size_;k1++) for (int k2=0;k2<v_size_;k2++)
							mat[v_size_*k1 + k2]=0;

			// 3) Call a function `block_call() to execute MV multiplication
			const float* ret = this->blockMV();

			// 4) Accumulate intermediate results
			for(int row = 0; row < block_row; ++row) output[i + row] += ret[row];
		}
	}
}

void FPGA::largeMM(const float* weight_mat, const float* input_mat, float* output, int num_input, int num_output, int num_matrix2)
{
	float* m1 = this->matrix_M1();
	float* m2 = this->matrix_M2();

	// 0) Initialize output vector		
	for(int i = 0; i < num_output*num_matrix2; ++i)
		output[i] = 0;

	for(int i = 0; i < num_output; i += v_size_)
	{
		for(int j = 0; j < num_input; j += v_size_)
		{			
			for(int k = 0; k < num_matrix2; k += v_size_)
			{
				// 0) Initialize input vector
				int block_row = min(v_size_, num_output-i);
				int block_col_1 = min(v_size_, num_input-j);
				int block_col_2 = min(v_size_, num_matrix2-k);

				int i1, j1, k1;
				// 1) Assign a m1
				for (i1=0;i1<block_row;i1++){
					for (j1=0;j1<block_col_1;j1++) m1[i1 * v_size_ + j1] = weight_mat[(i+i1) * (num_input) + (j+j1)];
					for (;j1<v_size_;j1++) m1[i1 * v_size_ + j1] = 0;
				}
				for (;i1<v_size_;i1++) for (j1=0;j1<v_size_;j1++)
					m1[i1 * v_size_ + j1] = 0;

				// 2) Assign a m2
				for (j1=0;j1<block_col_1;j1++){
					for (k1=0;k1<block_col_2;k1++) m2[j1 * v_size_ + k1] = input_mat[(j+j1) * (num_matrix2) + (k+k1)];
					for (;k1<v_size_;k1++) m2[j1 * v_size_ + k1] = 0;
				}
				for (;j1<v_size_;j1++) for (k1=0;k1<v_size_;k1++)
					m2[j1 * v_size_ + k1] = 0;

				// 3) Call a function `blockMM() to execute Matrix matrix multiplication
				const float* ret = this->blockMM();

				// 4) Accumulate intermediate results
				for(int n = 0; n<block_row; ++n)
				{
					for(int m = 0; m<block_col_2; ++m)
					{
						output[(i + n) + (k + m)*num_output] += ret[n*v_size_ + m];
					}
				}
				
			}
		} 
	}
}

void FPGA::convLowering(const std::vector<std::vector<std::vector<std::vector<float>>>>& cnn_weights,
		std::vector<std::vector<float>>& new_weights,
		const std::vector<std::vector<std::vector<float>>>& inputs,
		std::vector<std::vector<float>>& new_inputs) {
	/*
	 * Arguments:
	 *
	 * conv_weights: [conv_channel, input_channel, conv_height, conv_width]
	 * new_weights: [?, ?]
	 * inputs: [input_channel, input_height, input_width]
	 * new_inputs: [?, ?]
	 *
	 */

	int conv_channel = cnn_weights.size();
	int input_channel = cnn_weights[0].size();
	int conv_height = cnn_weights[0][0].size();
	int conv_width = cnn_weights[0][0][0].size();
	int input_height = inputs[0].size();
	int input_width = inputs[0][0].size();

	// IMPLEMENT THIS
	int ic,cc,i,j,i2,j2;
	for (ic=0;ic<input_channel;ic++){
		for (i=0;i<input_height - conv_height + 1;i++) for (j=0;j<input_width - conv_width + 1;j++){
			for (i2=0;i2<conv_height;i2++) for (j2=0;j2<conv_width;j2++){
				new_inputs[ic * (conv_height * conv_width) + i2 * (conv_width) + j2][i * (input_width - conv_width + 1) + j] = inputs[ic][i+i2][j+j2];
			}
		}
	}

	for (cc=0;cc<conv_channel;cc++){
		for (ic=0;ic<input_channel;ic++){
			for (i=0;i<conv_height;i++) for (j=0;j<conv_width;j++){
				new_weights[cc][ic * (conv_height * conv_width) + (i * conv_width + j)] = cnn_weights[cc][ic][i][j];
			}
		}
	}
}