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template_cpp_r7_bcnn.cpp
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template_cpp_r7_bcnn.cpp
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/*
* C Templete for a Binarized CNN
*
* Created on: 2017/07/01
* Author: H. Nakahara
*/
#include <stdio.h>
#include <stdlib.h>
#include <iostream>
#include <bitset>
#include <ap_int.h>
#ifdef __SDSCC__
#include "sds_lib.h"
#else
#define sds_alloc(x)(malloc(x))
#define sds_free(x)(free(x))
#endif
// custom bitwidth for streaming operation
typedef ap_int<2> bit_2;
typedef ap_int<4> bit_4;
typedef ap_int<8> bit_8;
typedef ap_int<16> bit_16;
typedef ap_int<32> bit_32;
typedef ap_int<64> bit_64;
typedef ap_int<128> bit_128;
typedef ap_int<256> bit_256;
typedef ap_int<512> bit_512;
// weight memory -----------------------------------------------------------
(DEF_WEIGHT_MEM)
// bias memory ------------------------------------------------------------
(DEF_BIAS_MEM)
// -------------------------------------------------------------------------
// Load weights and bias from the external memory (DDR3/4 Memory)
// -------------------------------------------------------------------------
#ifdef __SDSCC__
#pragma SDS data access_pattern(t_bin_convW: SEQUENTIAL)
#pragma SDS data access_pattern(t_BNFb: SEQUENTIAL)
#pragma SDS data zero_copy(t_bin_convW[0:(WEIGHT_SIZ)])
#pragma SDS data zero_copy(t_BNFb[0:(BIAS_SIZ)])
#endif
void setup(
#ifdef __SDSCC__
int *t_bin_convW,
int *t_BNFb
#else
int t_bin_convW[(WEIGHT_SIZ)],
int t_BNFb[(BIAS_SIZ)]
#endif
)
{
// set buffer memory -----------------------------------------------
int x, y, of, inf, offset;
// -----------------------------------------------------------------
// setup memory
// -----------------------------------------------------------------
(SET_WEIGHT_MEM)
(SET_BIAS_MEM)
}
// -------------------------------------------------------------------------
// Binary Convolutional Layer
// -------------------------------------------------------------------------
void bin_conv2d_pipeline(
ap_int<(MAX_BCONV_WIDTH)> fmap[(IMGSIZ)][(IMGSIZ)],
int layer,
int size,
int n_in,
int n_out
)
{
(BCONV_REG_PRAGMA)
int ofeat, infeat, w_flag;
int i, k, ky, kx, ix, iy, ox, oy;
int idx = 0;
static ap_int<(MAX_BCONV_WIDTH)> shift_reg1[((IMGSIZ) 2)*3];
#pragma HLS ARRAY_PARTITION variable=shift_reg1 complete dim=1
static ap_uint<1> padding_shift_reg[((IMGSIZ) 2)*3];
#pragma HLS ARRAY_PARTITION variable=padding_shift_reg complete dim=1
int cnt = 0;
ix = iy = ox = oy = w_flag = 0;
CONV_IF: for( k = 0; k < (size 2) * (size 2); k ){
#pragma HLS loop_flatten off
SHIFT_REG: for( i = 0; i < 2 * ((IMGSIZ) 2) 3; i ){
#pragma HLS UNROLL
shift_reg1[ i] = shift_reg1[ i 1];
padding_shift_reg[ i] = padding_shift_reg[ i 1];
}
ap_int<(MAX_BCONV_WIDTH)> din;
ap_uint<1> padding;
if( (ix > 0 && ix <= size) && (iy > 0 && iy <= size)){
din = (ap_int<(MAX_BCONV_WIDTH)>)fmap[iy-1][ix-1];
padding = 0;
} else {
ap_int<(MAX_BCONV_WIDTH)> allone;
allone = ~0;
din = allone;
padding = 1;
}
switch( layer){
(BCONV_REG_SELECT)
}
ix ;
if( ix == size 2){
ix = 0;
iy ;
}
if( k >= ((size 2)*2 3 - 1)){
w_flag ;
if( w_flag > (size 2)){
w_flag = 1;
cnt = 0;
}
}
// convolutional operation -----------------------------------
ap_uint<(MAX_BCONV_WIDTH)> bit_tmp = 0x1;
ap_uint<(MAX_BCONV_WIDTH)> streamOut = 0;
OF: for( ofeat = 0; ofeat < n_out; ofeat ){
ap_int<16> tmp = 0;
ap_int<16> tmp2;
CONV_KY: for( ky = 0; ky < 3; ky ){
#pragma HLS pipeline
CONV_KX: for( kx = 0; kx < 3; kx ){
ap_uint<(MAX_BCONV_WIDTH)> bx, bw;
ap_uint<(MAX_BCONV_WIDTH)> bxor;
ap_uint<(MAX_BCONV_WIDTH)> mask;
ap_uint<(MAX_BCONV_WIDTH)> allzero = 0;
ap_uint<1>is_padding;
switch( layer){
(BCONV_WEIGHT_SELECT)
}
(BIN_XOR_MAC)
tmp2 = 0;
ONES_COUNT: for( i = 0; i < (MAX_BCONV_WIDTH); i ){
tmp2 = (((bxor >> i) & 0x1) == 1) ? 1 : 0;
}
if( is_padding == 0)
tmp = (n_in - tmp2 * 2);
}
}
if( w_flag > 0 && w_flag <= size){
#pragma HLS pipeline
ap_int<16> bias;
switch( layer){
(BCONV_BIAS_SELECT)
}
tmp = bias;
if( tmp >= 0) streamOut = streamOut | bit_tmp;
bit_tmp = bit_tmp << 1;
cnt ;
if( cnt == n_out){
cnt = 0;
fmap[oy][ox] = (ap_int<(MAX_BCONV_WIDTH)>)streamOut;
ox ;
if( ox == size){
ox = 0;
oy ;
}
idx ;
}
}
}
}
}
// ------------------------------------------------------------------------
template< typename BIN_TYPE, typename BOUT_TYPE, int N_IFEAT, int N_OFEAT, int IF_SIZ, int OF_SIZ>
void int_conv2d_pipeline(
BIN_TYPE infmap[IF_SIZ][IF_SIZ],
BOUT_TYPE outfmap[OF_SIZ][OF_SIZ],
ap_int<(NUMIMG)> W[N_OFEAT][3*3],
ap_int<20> BNFb[N_OFEAT]
)
{
#pragma HLS ARRAY_PARTITION variable=W cyclic factor=9 dim=2
int ofeat, infeat;
int w_flag;
int i, k, ky, kx;
int idx = 0;
static ap_int<N_IFEAT> shift_reg1[(IF_SIZ 2)*3];
#pragma HLS ARRAY_PARTITION variable=shift_reg1 complete dim=1
int cnt = 0;
int debug_out = 0;
w_flag = 0;
int ix, iy, ox, oy;
ix = iy = ox = oy = 0;
CONV_IF: for( k = 0; k < (IF_SIZ 2) * (IF_SIZ 2); k ){
#pragma HLS loop_flatten off
// pipeline register ------------------------------------------
SHIFT_REG: for( i = 0; i < 2 * (IF_SIZ 2) 3; i ){
#pragma HLS UNROLL
shift_reg1[ i] = shift_reg1[ i 1];
}
ap_int<N_IFEAT> din;
if( (ix > 0 && ix <= IF_SIZ) && (iy > 0 && iy <= IF_SIZ)){
din = infmap[iy-1][ix-1];
} else {
ap_int<N_IFEAT> allzero;
allzero = 0;
din = allzero;
}
shift_reg1[ 2 * (IF_SIZ 2) 3 - 1] = din;
ix ;
if( ix == IF_SIZ 2){
ix = 0;
iy ;
}
// enable MAC operation
if( k >= ((IF_SIZ 2)*2 3 - 1)){
w_flag ;
if( w_flag > (IF_SIZ 2)){
w_flag = 1;
cnt = 0;
}
}
// convolutional operation -----------------------------------
ap_uint<N_OFEAT>bit_tmp = 0x1;
ap_uint<N_OFEAT> streamOut = 0;
OF: for( ofeat = 0; ofeat < N_OFEAT; ofeat ){
int tmp = 0;
ap_int<20> tmp2;
CONV_KY: for( ky = 0; ky < 3; ky ){
#pragma HLS pipeline
CONV_KX: for( kx = 0; kx < 3; kx ){
ap_int<64> bx;
ap_int<3> bw;
bx = shift_reg1[ky * (IF_SIZ 2) kx];
bw = W[ofeat][ky*3 kx];
MAC_RGB: for( i = 0; i < 3; i ){
tmp2 = ap_int<20>(bx & 0xFFFFF);
tmp = ((bw & 0x1) == 0) ? (tmp - (int)tmp2) : (tmp (int)tmp2);
bw = bw >> 1;
bx = bx >> 20;
}
}
}
// output to Streaming Buffer
if( w_flag > 0 && w_flag <= IF_SIZ){
#pragma HLS pipeline
tmp = BNFb[ofeat];
if( tmp >= 0) streamOut = streamOut | bit_tmp;
bit_tmp = bit_tmp << 1;
cnt ;
if( cnt == N_OFEAT){
cnt = 0;
outfmap[oy][ox] = streamOut;
ox ;
if( ox == OF_SIZ){
ox = 0;
oy ;
}
idx ;
}
}
}
}
}
template< typename BIN_TYPE, typename BOUT_TYPE, int NUM_IFEAT, int NUM_OFEAT,
int INFEAT_SIZ, int OFEAT_SIZ>
void int_conv2d_layer(
BIN_TYPE infmap[INFEAT_SIZ][INFEAT_SIZ],
BOUT_TYPE outfmap[OFEAT_SIZ][OFEAT_SIZ],
ap_int<(NUMIMG)> W[NUM_OFEAT][3*3],
ap_int<20> BNFb[NUM_OFEAT]
)
{
int_conv2d_pipeline< BIN_TYPE, BOUT_TYPE, NUM_IFEAT, NUM_OFEAT,
INFEAT_SIZ, OFEAT_SIZ>( infmap, outfmap, W, BNFb);
}
// -------------------------------------------------------------------------
// Maximum Pooling Layer
// -------------------------------------------------------------------------
template< typename TYPE_BIT, int FEAT_SIZ, int POOL_SIZ>
void max_pooling_layer( TYPE_BIT ftmp[FEAT_SIZ][FEAT_SIZ])
{
int inf_x, inf_y, oy, ox;
TYPE_BIT tmp0, tmp1, tmp2, tmp3, m;
oy = 0;
PY: for( inf_y = 0; inf_y < FEAT_SIZ; inf_y = 2){
ox = 0;
PX: for( inf_x = 0; inf_x < FEAT_SIZ; inf_x = 2){
tmp0 = ftmp[inf_y][inf_x];
tmp1 = ftmp[inf_y][inf_x 1];
tmp2 = ftmp[inf_y 1][inf_x];
tmp3 = ftmp[inf_y 1][inf_x 1];
m = tmp0 | tmp1 | tmp2 | tmp3;
ftmp[oy][ox] = m;
ox ;
}
oy ;
}
}
// -------------------------------------------------------------------------
// FC Layer
// -------------------------------------------------------------------------
template < int NUM_OFEAT, int NUM_INFEAT>
void fc_layer(
ap_int<1> fc_tmp[NUM_INFEAT],
ap_int<1> lW[NUM_OFEAT][NUM_INFEAT],
ap_int<16> b_BNFb[NUM_OFEAT],
int fc_result[(MAX_DENSE_SIZ)]
)
{
int ofeat, tmp, infeat;
FC_O: for( ofeat = 0; ofeat < NUM_OFEAT; ofeat ){
#pragma HLS LOOP_FLATTEN off
tmp = 0;
FC_I: for( infeat = 0; infeat < NUM_INFEAT; infeat ){
#pragma HLS pipeline
ap_int<1> bw, bx, xnor;
bw = lW[ofeat][infeat];
bx = fc_tmp[infeat];
xnor = ~(bw ^ bx);
tmp = (xnor == 0) ? -1 : 1;
}
fc_result[ofeat] = tmp b_BNFb[ofeat];
}
}
// -------------------------------------------------------------------------
// Binarized CNN Kernel
// -------------------------------------------------------------------------
#ifdef __SDSCC__
#pragma SDS data access_pattern(t_in_img: SEQUENTIAL)
#pragma SDS data zero_copy(t_in_img[0:(IMGSIZ)*(IMGSIZ)])
#endif
void kernel(
#ifdef __SDSCC__
ap_int<64> t_in_img[(IMGSIZ)*(IMGSIZ)],
int fc_result[10]
#else
ap_int<64> t_in_img[(IMGSIZ)*(IMGSIZ)],
int fc_result[10]
#endif
)
{
ap_int<(MAX_BCONV_WIDTH)> fb_tmp[(IMGSIZ)][(IMGSIZ)];
ap_int<1> fc_tmp[(MAX_DENSE_SIZ)];
ap_int<64> in_img[(IMGSIZ)][(IMGSIZ)];
int y, x, of, layer, bin_layer_idx;
(DEF_CNN_PARAMETER)
for( y = 0; y < (IMGSIZ); y ){
for( x = 0; x < (IMGSIZ); x ){
in_img[y][x] = t_in_img[y*(IMGSIZ) x];
}
}
#pragma HLS INLINE
bin_layer_idx = 1;
BCONV: for( layer = 0; layer < (NUM_LAYER); layer ){
switch(layer){
(DEF_CNN_LAYER)
}
}
}
//--------------------------------------------------------------------
// Top Function for a Binarized CNN
//--------------------------------------------------------------------
#ifdef __SDSCC__
#pragma SDS data access_pattern(t_bin_convW: SEQUENTIAL)
#pragma SDS data access_pattern(t_BNFb: SEQUENTIAL)
#pragma SDS data access_pattern(t_in_img: SEQUENTIAL)
#pragma SDS data zero_copy(t_bin_convW[0:(WEIGHT_SIZ)])
#pragma SDS data zero_copy(t_BNFb[0:(BIAS_SIZ)])
#pragma SDS data zero_copy(t_in_img[0:(IMGSIZ)*(IMGSIZ)])
#endif
void BinCNN(
#ifdef __SDSCC__
int *t_bin_convW,
int *t_BNFb,
ap_int<64> t_in_img[(IMGSIZ)*(IMGSIZ)],
int fc_result[(OUT_DENSE_SIZ)],
int init
#else
int t_bin_convW[(WEIGHT_SIZ)],
int t_BNFb[(BIAS_SIZ)],
ap_int<64> t_in_img[(IMGSIZ)*(IMGSIZ)],
int fc_result[(OUT_DENSE_SIZ)],
int init
#endif
)
{
/*
#pragma HLS INTERFACE s_axilite register port=t_bin_convW bundle=slv0
#pragma HLS INTERFACE s_axilite register port=t_BNFb bundle=slv0
#pragma HLS INTERFACE s_axilite register port=t_in_img bundle=slv0
#pragma HLS INTERFACE s_axilite register port=fc_result bundle=slv0
#pragma HLS INTERFACE s_axilite register port=init bundle=slv0
#pragma HLS INTERFACE s_axilite register port=return bundle=slv0
*/
if( init == 1)
setup( t_bin_convW, t_BNFb);
else
kernel( t_in_img, fc_result);
}
// ------------------------------------------------------------------
// END OF PROGRAM
// ------------------------------------------------------------------