Adding benchmarks to branch_jc

4 files changed, 729 insertions, 0 deletions

diff --git a/benchmarks/kmeans/lloyds_algorithm_top.cpp b/benchmarks/kmeans/lloyds_algorithm_top.cpp
new file mode 100644
index 0000000..f2db1ce
--- /dev/null
+++ b/benchmarks/kmeans/lloyds_algorithm_top.cpp
@@ -0,0 +1,326 @@
+// source: https://github.com/FelixWinterstein/Vivado-KMeans/tree/b1121f826bdac8db9502e4bf0c8f3b08425bc061/lloyds_algorithm_HLS/source
+
+/**********************************************************************
+* Felix Winterstein, Imperial College London
+*
+* File: lloyds_algorithm_top.cpp
+*
+* Revision 1.01
+* Additional Comments: distributed under a BSD license, see LICENSE.txt
+*
+**********************************************************************/
+
+#include "lloyds_algorithm_top.h"
+#include "lloyds_algorithm_util.h"
+
+
+// global array for the data (keep it local to this file)
+data_type data_int_memory[N];
+data_type centre_positions[K*P];
+centre_type centre_buffer[K*P];
+
+
+// top-level function of the design
+void lloyds_algorithm_top(  volatile data_type *data,
+                            volatile data_type *cntr_pos_init,
+                            node_pointer n,
+                            centre_index_type k,
+                            volatile coord_type_ext *distortion_out,
+                            volatile data_type *clusters_out)
+{
+    // set the interface properties
+    #pragma HLS interface ap_none register port=n
+    #pragma HLS interface ap_none register port=k
+    #pragma HLS interface ap_fifo port=data depth=256
+
+    #pragma HLS interface ap_fifo port=cntr_pos_init depth=256
+    #pragma HLS interface ap_fifo port=distortion_out depth=256
+    #pragma HLS interface ap_fifo port=clusters_out depth=256
+
+	/*
+    #pragma HLS data_pack variable=data
+    #pragma HLS data_pack variable=cntr_pos_init
+    #pragma HLS data_pack variable=clusters_out
+    */
+    #pragma HLS data_pack variable=data_int_memory
+    #pragma HLS data_pack variable=centre_positions
+    #pragma HLS data_pack variable=centre_buffer
+
+    // specify the type of memory instantiated for these arrays: two-port block ram
+    #pragma HLS resource variable=data_int_memory core=RAM_2P_BRAM
+    #pragma HLS resource variable=centre_positions core=RAM_2P_BRAM
+    #pragma HLS resource variable=centre_buffer core=RAM_2P_LUTRAM
+
+    // partition the arrays according to the parallelism degree P
+    // NOTE: the part. factor must be updated if P is changed (in lloyds_alogrithm_top.h) !
+    #pragma HLS array_partition variable=centre_buffer block factor=40 dim=1
+    #pragma HLS array_partition variable=centre_positions block factor=40 dim=1
+
+    init_node_memory(data,n);
+
+    centre_type filt_centres_out[K];
+    data_type new_centre_positions[K];
+    // more struct-packing
+    #pragma HLS data_pack variable=filt_centres_out
+    #pragma HLS data_pack variable=filt_centres_out
+
+    // iterate over a constant number of outer clustering iterations
+    it_loop: for (uint l=0; l<L; l++) {
+
+        for (centre_index_type i=0; i<=k; i++) {
+            data_type tmp_pos;
+            if (l==0) {
+                tmp_pos = cntr_pos_init[i];
+            } else {
+                tmp_pos = new_centre_positions[i];
+            }
+            for (uint p=0; p<P; p++) {
+                #pragma HLS unroll
+                centre_positions[p*K+i] = tmp_pos;
+            }
+            if (i==k) {
+                break;
+            }
+        }
+
+        // run the clustering kernel
+        lloyds(n, k, filt_centres_out);
+
+        // re-init centre positions
+        update_centres(filt_centres_out, k, new_centre_positions);
+
+    }
+
+    // write clustering output: new cluster centres and distortion
+    output_loop: for (centre_index_type i=0; i<=k; i++) {
+        #pragma HLS pipeline II=1
+        distortion_out[i] = filt_centres_out[i].sum_sq;
+        clusters_out[i].value = new_centre_positions[i].value;
+        if (i==k) {
+            break;
+        }
+    }
+}
+
+
+// load data points from interface into internal memory
+void init_node_memory(volatile data_type *node_data, node_pointer n)
+{
+    #pragma HLS inline
+    for (node_pointer i=0; i<=n; i++) {
+        #pragma HLS pipeline II=1
+        data_int_memory[i] = node_data[i];
+        if (i==n) {
+            break;
+        }
+    }
+}
+
+
+// update the new centre positions after one outer clustering iteration
+void update_centres(centre_type *centres_in,centre_index_type k, data_type *centres_positions_out)
+{
+    #pragma HLS inline
+    centre_update_loop: for (centre_index_type i=0; i<=k; i++) {
+        #pragma HLS pipeline II=1
+        centre_type tmp_cent = Reg(centres_in[i]);
+        coord_type tmp_count = tmp_cent.count;
+        if ( tmp_count == 0 )
+            tmp_count = 1;
+
+        data_type_ext tmp_wgtCent = tmp_cent.wgtCent;
+        data_type tmp_new_pos;
+        for (uint d=0; d<D; d++) {
+            #pragma HLS unroll
+            coord_type_ext tmp_div_ext = (get_coord_type_vector_ext_item(tmp_wgtCent.value,d) / tmp_count); //let's see what it does with that...
+            coord_type tmp_div = (coord_type) tmp_div_ext;
+            #pragma HLS resource variable=tmp_div core=DivnS
+            set_coord_type_vector_item(&tmp_new_pos.value,Reg(tmp_div),d);
+        }
+        centres_positions_out[i] = Reg(tmp_new_pos);
+        if (i==k) {
+            break;
+        }
+    }
+}
+
+
+// main clustering kernel
+void lloyds (   node_pointer n,
+                centre_index_type k,
+                centre_type *centres_out)
+{
+    // register ports of this entity
+    #pragma HLS interface port=n register
+    #pragma HLS interface port=k register
+    #pragma HLS interface port=return register
+    #pragma HLS interface ap_memory register port=data_int_memory
+    #pragma HLS interface ap_memory register port=centre_positions
+
+    // init centre buffer
+    init_centre_buffer_loop: for(centre_index_type i=0; i<=k; i++) {
+        #pragma HLS pipeline II=1
+
+        for (uint p=0; p<P;p++) {
+            #pragma HLS unroll
+            centre_buffer[i+K*p].count = 0;
+            centre_buffer[i+K*p].sum_sq = 0;
+            data_type_ext tmp;
+            for (uint d=0; d<D; d++) {
+                #pragma HLS unroll
+                set_coord_type_vector_ext_item(&tmp.value,0,d);
+            }
+            centre_buffer[i+K*p].wgtCent = tmp;
+        }
+
+        if (i==k) {
+            #pragma HLS occurrence cycle=2
+            break;
+        }
+    }
+
+    data_type u[P];
+    #pragma HLS array_partition variable=u complete
+
+    // iterate over all data points
+    process_data_points_loop: for (node_pointer i=0; i<=n; i+=P) {
+
+        data_fetch_loop: for (uint p=0; p<P; p++) {
+            #pragma HLS pipeline II=1
+            u[p] = data_int_memory[i+p];
+        }
+
+        centre_index_type final_centre_index[P];
+        coord_type_ext sum_sq_out[P];
+
+        // iterate over all centres
+        minsearch_loop: for (centre_index_type ii=0; ii<=k; ii++) {
+            #pragma HLS pipeline II=1
+
+            par_loop: for (uint p=0; p<P; p++) {
+                #pragma HLS unroll
+                coord_type_ext min_dist[P];
+
+                #pragma HLS array_partition variable=final_centre_index complete
+                #pragma HLS array_partition variable=sum_sq_out complete
+                #pragma HLS array_partition variable=min_dist complete
+
+                // help the scheduler by declaring inter-iteration dependencies for these variables as false
+                #pragma HLS dependence variable=u inter false
+                #pragma HLS dependence variable=final_centre_index inter false
+                #pragma HLS dependence variable=sum_sq_out inter false
+                #pragma HLS dependence variable=min_dist inter false
+                #pragma HLS dependence variable=centre_buffer inter false
+                #pragma HLS dependence variable=centre_positions inter false
+
+                if (ii==0) {
+                    min_dist[p]=MAX_FIXED_POINT_VAL_EXT;
+                }
+
+                coord_type_ext tmp_dist;
+                compute_distance(centre_positions[p*K+ii], u[p], &tmp_dist);
+
+                // select the centre with the smallest distance to the data point
+                if (tmp_dist<min_dist[p]) {
+                    min_dist[p] = tmp_dist;
+                    final_centre_index[p] = ii;
+                    sum_sq_out[p] = tmp_dist;
+                }
+
+                //printf("%d: i=%d, %d, %d\n",p,i.VAL+p,final_centre_index[p].VAL,sum_sq_out[p].VAL);
+
+                if (ii == k) {
+                    // trigger at most every other cycle
+                    #pragma HLS occurrence cycle=2
+                    if (p==P-1) {
+                        break;
+                    }
+                }
+            }
+        }
+
+        // after minsearch loop: update centre buffer
+        for (uint p=0; p<P; p++) {
+            #pragma HLS unroll
+            data_type_ext tmp_wgtCent;
+            coord_type_ext tmp1, tmp2, tmp3, tmp4, tmp5, tmp6;
+            uint tmp_idx = (final_centre_index[p]+K*p);
+            // weighted centroid of this centre
+            for (uint d=0; d<D; d++) {
+                #pragma HLS unroll
+                tmp1 = get_coord_type_vector_ext_item(centre_buffer[(tmp_idx)].wgtCent.value,d);
+                //if (i+p<=n) {
+                    tmp2 = (coord_type_ext)get_coord_type_vector_item(u[p].value,d);
+                //} else {
+                    //tmp2 = 0;
+                //}
+                set_coord_type_vector_ext_item(&tmp_wgtCent.value,Reg(tmp1)+Reg(tmp2),d);
+            }
+            centre_buffer[tmp_idx].wgtCent = tmp_wgtCent;
+
+            // update number of points assigned to centre
+            tmp4 =  centre_buffer[(tmp_idx)].count;
+            //if (i+p<=n) {
+                tmp3 =  1;
+            //} else {
+                //tmp3 =  0;
+            //}
+            centre_buffer[tmp_idx].count = Reg(tmp3) + Reg(tmp4);
+
+            //if (i+p<=n) {
+                tmp5 =  sum_sq_out[p];
+            //} else {
+                //tmp5 =  0;
+            //}
+            tmp6 =  centre_buffer[(tmp_idx)].sum_sq;
+            centre_buffer[tmp_idx].sum_sq  = Reg(tmp5) + Reg(tmp6);
+        }
+
+        if (i>=n-P+1) {
+        //if (i>=n) {
+            break;
+        }
+    }
+
+
+    // readout centres
+    read_out_centres_loop: for(centre_index_type i=0; i<=k; i++) {
+        #pragma HLS pipeline II=1
+
+        coord_type_ext arr_count[P];
+        coord_type_ext arr_sum_sq[P];
+        coord_type_vector_ext arr_wgtCent[P];
+        #pragma HLS array_partition variable=arr_count complete
+        #pragma HLS array_partition variable=arr_sum_sq complete
+        #pragma HLS array_partition variable=arr_wgtCent complete
+
+        for (uint p=0; p<P; p++) {
+            #pragma HLS unroll
+            arr_count[p] = ((coord_type_ext)centre_buffer[i+K*p].count);
+            arr_sum_sq[p] = (centre_buffer[i+K*p].sum_sq);
+            arr_wgtCent[p] = (centre_buffer[i+K*p].wgtCent.value);
+        }
+
+        centres_out[i].count = tree_adder(arr_count,P);
+        //printf("%d\n",centres_out[i].count.VAL);
+        centres_out[i].sum_sq = tree_adder(arr_sum_sq,P);
+        coord_type_vector_ext tmp_sum;
+        for (uint d=0; d<D; d++) {
+            #pragma HLS unroll
+            coord_type_ext tmp_a[P];
+            for (uint p=0; p<P; p++) {
+                #pragma HLS unroll
+                tmp_a[p] = get_coord_type_vector_ext_item(arr_wgtCent[p],d);
+            }
+            coord_type_ext tmp = tree_adder(tmp_a,P);
+            set_coord_type_vector_ext_item(&tmp_sum,tmp,d);
+        }
+        centres_out[i].wgtCent.value = tmp_sum;
+
+        if (i==k) {
+            break;
+        }
+    }
+
+}
+
diff --git a/benchmarks/kmeans/lloyds_algorithm_top.h b/benchmarks/kmeans/lloyds_algorithm_top.h
new file mode 100644
index 0000000..1ea372e
--- /dev/null
+++ b/benchmarks/kmeans/lloyds_algorithm_top.h
@@ -0,0 +1,112 @@
+/**********************************************************************
+* Felix Winterstein, Imperial College London
+*
+* File: lloyds_algorithm_top.h
+*
+* Revision 1.01
+* Additional Comments: distributed under a BSD license, see LICENSE.txt
+*
+**********************************************************************/
+
+
+#ifndef LLOYDS_ALGORITHM_TOP_H
+#define LLOYDS_ALGORITHM_TOP_H
+
+#include <math.h>
+#include "ap_int.h" // custom data types
+
+#define D 3         // data dimensionality
+#define N 32768     // max. number of data points
+#define K 256       // max. number of centres
+#define L 6         // max. number of iterations
+#define P 40        // parallelisation degree
+
+#define COORD_BITWIDTH 16
+#define COORD_BITWITDH_EXT 32
+#define NODE_POINTER_BITWIDTH 15    // log2(N)
+#define CNTR_INDEX_BITWIDTH 8       // log2(K)
+
+// pointer types to tree nodes and centre lists
+typedef ap_uint<NODE_POINTER_BITWIDTH> node_pointer;
+typedef ap_uint<CNTR_INDEX_BITWIDTH> centre_index_type;
+
+// force register insertion in the generated RTL for some signals
+#define FORCE_REGISTERS
+// ... used for saturation
+#define MAX_FIXED_POINT_VAL_EXT (1<<(COORD_BITWITDH_EXT-1))-1
+
+typedef unsigned int uint;
+typedef ap_int<COORD_BITWIDTH> coord_type;
+typedef ap_int<D*COORD_BITWIDTH> coord_type_vector;
+typedef ap_int<COORD_BITWITDH_EXT> coord_type_ext;
+typedef ap_int<D*COORD_BITWITDH_EXT> coord_type_vector_ext;
+
+//bit width definitions for multiplications
+#define MUL_INTEGER_BITS 12
+#define MUL_FRACTIONAL_BITS 6
+#define MUL_MAX_VAL (1<<(MUL_INTEGER_BITS+MUL_FRACTIONAL_BITS-1))-1
+#define MUL_MIN_VAL -1*(1<<(MUL_INTEGER_BITS+MUL_FRACTIONAL_BITS-1))
+typedef ap_int<MUL_INTEGER_BITS+MUL_FRACTIONAL_BITS> mul_input_type;
+
+// this should be always 1
+#define FILE_INDEX 1
+
+// data point types
+struct data_type {
+    //coord_type value[D];
+    coord_type_vector value;
+    data_type& operator=(const data_type& a);
+    data_type& operator=(const volatile data_type& a);
+};
+
+
+// data point types ext
+struct data_type_ext {
+    coord_type_vector_ext value;
+    data_type_ext& operator=(const data_type_ext& a);
+};
+
+
+// centre types
+struct centre_type {
+    data_type_ext wgtCent; // sum of all points assigned to this centre
+    coord_type_ext sum_sq; // sum of norm of all points assigned to this centre
+    coord_type count;
+    centre_type& operator=(const centre_type& a);
+};
+typedef centre_type* centre_ptr;
+
+
+#ifdef FORCE_REGISTERS
+template<class T>
+T Reg(T in) {
+    #pragma HLS INLINE off
+    #pragma HLS INTERFACE port=return register
+    return in;
+}
+#else
+template<class T>
+T Reg(T in) {
+    #pragma HLS INLINE
+    return in;
+}
+#endif
+
+
+
+void lloyds_algorithm_top(  volatile data_type *data,
+                            volatile data_type *cntr_pos_init,
+                            node_pointer n,
+                            centre_index_type k,
+                            volatile coord_type_ext *distortion_out,
+                            volatile data_type *clusters_out);
+
+void init_node_memory(volatile data_type *node_data, node_pointer n);
+
+void update_centres(centre_type *centres_in,centre_index_type k, data_type *centres_positions_out);
+
+void lloyds (   node_pointer n,
+                centre_index_type k,
+                centre_type *centres_out);
+
+#endif  /* LLOYDS_ALGORITHM_TOP_H */
diff --git a/benchmarks/kmeans/lloyds_algorithm_util.cpp b/benchmarks/kmeans/lloyds_algorithm_util.cpp
new file mode 100644
index 0000000..fa1db19
--- /dev/null
+++ b/benchmarks/kmeans/lloyds_algorithm_util.cpp
@@ -0,0 +1,258 @@
+/**********************************************************************
+* Felix Winterstein, Imperial College London
+*
+* File: lloyds_algorithm_util.cpp
+*
+* Revision 1.01
+* Additional Comments: distributed under a BSD license, see LICENSE.txt
+*
+**********************************************************************/
+
+#include <math.h>
+#include "lloyds_algorithm_util.h"
+
+
+data_type& data_type::operator=(const data_type& a)
+{
+
+    value = a.value;
+    return *this;
+}
+
+data_type& data_type::operator=(const volatile data_type& a)
+{
+
+    value = a.value;
+    return *this;
+}
+
+
+
+data_type_ext& data_type_ext::operator=(const data_type_ext& a)
+{
+    value = a.value;
+    return *this;
+}
+
+
+
+centre_type& centre_type::operator=(const centre_type& a)
+{
+    count = a.count;
+    wgtCent = a.wgtCent;
+    sum_sq = a.sum_sq;
+    //position = a.position;
+    return *this;
+}
+
+
+void set_coord_type_vector_item(coord_type_vector *a, const coord_type b, const uint idx)
+{
+    #pragma HLS function_instantiate variable=idx
+    a->range((idx+1)*COORD_BITWIDTH-1,idx*COORD_BITWIDTH) = b;
+}
+
+
+void set_coord_type_vector_ext_item(coord_type_vector_ext *a, const coord_type_ext b, const uint idx)
+{
+    #pragma HLS function_instantiate variable=idx
+    a->range((idx+1)*COORD_BITWITDH_EXT-1,idx*COORD_BITWITDH_EXT) = b;
+}
+
+
+coord_type get_coord_type_vector_item(const coord_type_vector a, const uint idx)
+{
+    #pragma HLS function_instantiate variable=idx
+    coord_type tmp= a.range((idx+1)*COORD_BITWIDTH-1,idx*COORD_BITWIDTH);
+    return tmp;
+}
+
+
+coord_type_ext get_coord_type_vector_ext_item(const coord_type_vector_ext a, const uint idx)
+{
+    #pragma HLS function_instantiate variable=idx
+    coord_type_ext tmp= a.range((idx+1)*COORD_BITWITDH_EXT-1,idx*COORD_BITWITDH_EXT);
+    return tmp;
+}
+
+
+/* ****** helper functions *******/
+
+
+// conversion from data_type_ext to data_type
+data_type conv_long_to_short(data_type_ext p)
+{
+    #pragma HLS inline
+    data_type result;
+    for (uint d=0; d<D; d++) {
+        #pragma HLS unroll
+        coord_type tmp = (coord_type)get_coord_type_vector_ext_item(p.value,d);
+        set_coord_type_vector_item(&result.value,tmp,d);
+    }
+    return result;
+}
+
+// conversion from data_type to data_type_ext
+data_type_ext conv_short_to_long(data_type p)
+{
+    #pragma HLS inline
+    data_type_ext result;
+    for (uint d=0; d<D; d++) {
+        #pragma HLS unroll
+        coord_type_ext tmp = (coord_type_ext)get_coord_type_vector_item(p.value,d);
+        set_coord_type_vector_ext_item(&result.value,tmp,d);
+    }
+    return result;
+}
+
+
+mul_input_type saturate_mul_input(coord_type_ext val)
+{
+    #pragma HLS inline
+    if (val > MUL_MAX_VAL) {
+        val = MUL_MAX_VAL;
+    } else if (val < MUL_MIN_VAL) {
+        val = MUL_MIN_VAL;
+    }
+    return (mul_input_type)val;
+}
+
+
+// fixed point multiplication with saturation and scaling
+coord_type_ext fi_mul(coord_type_ext op1, coord_type_ext op2)
+{
+    #pragma HLS inline
+    mul_input_type tmp_op1 = saturate_mul_input(op1);
+    mul_input_type tmp_op2 = saturate_mul_input(op2);
+
+    ap_int<2*(MUL_INTEGER_BITS+MUL_FRACTIONAL_BITS)> result_unscaled;
+    result_unscaled = tmp_op1*tmp_op2;
+    #pragma HLS resource variable=result_unscaled core=MulnS
+
+    ap_int<2*(MUL_INTEGER_BITS+MUL_FRACTIONAL_BITS)> result_scaled;
+    result_scaled = result_unscaled >> MUL_FRACTIONAL_BITS;
+    coord_type_ext result;
+    result = (coord_type_ext)result_scaled;
+    return result;
+}
+
+// tree adder
+coord_type_ext tree_adder(coord_type_ext *input_array,const uint m)
+{
+    #pragma HLS inline
+
+    for(uint j=0;j<ceil(log2(m));j++)
+    {
+        #pragma HLS unroll
+        //printf("j = %d\n",j);
+        if (j<ceil(log2(m))-1) {
+            for(uint i = 0; i < uint((m+m-uint(m/(1<<(j)))*(1<<(j)))/(1<<(j+1))); i++)
+            {
+                #pragma HLS unroll
+                coord_type_ext tmp1 = input_array[2*i];
+                coord_type_ext tmp2 = input_array[2*i+1];
+                coord_type_ext tmp3 = tmp1+tmp2;
+                input_array[i] = tmp3;
+                #pragma HLS resource variable=tmp3 core=AddSubnS
+                //printf("[%d] = [%d]+[%d]\n",i,2*i,2*i+1);
+            }
+            if (m > uint((m+m-uint(m/(1<<(j)))*(1<<(j)))/(1<<(j+1)))*(1<<(j+1))) {
+                input_array[uint(m/(1<<(j+1)))] = input_array[uint(m/(1<<(j+1))-1)*2+2];
+                //printf("[%d] = [%d]\n",uint(m/(1<<(j+1))),uint(m/(1<<(j+1))-1)*2+2);
+            }
+        }
+        if (j== ceil(log2(m))-1) {
+            coord_type_ext tmp1 = input_array[0];
+            coord_type_ext tmp2 = input_array[1];
+            coord_type_ext tmp3 = tmp1+tmp2;
+            input_array[0] = tmp3;
+            //printf("[%d] = [%d]+[%d]\n",0,0,1);
+            #pragma HLS resource variable=tmp3 core=AddSubnS
+        }
+    }
+    return input_array[0];
+}
+
+
+// tree compare select
+void tree_cs(coord_type_ext *input_array,centre_index_type *index_array,coord_type_ext *res_val,centre_index_type *res_idx,const uint m)
+{
+    #pragma HLS inline
+
+    for(uint j=0;j<ceil(log2(m));j++)
+    {
+        if (j<ceil(log2(m))-1) {
+            for(uint i = 0; i < uint(m/(1<<(j+1))); i++)
+            {
+                coord_type_ext tmp1 = input_array[2*i];
+                coord_type_ext tmp1_idx = index_array[2*i];
+                coord_type_ext tmp2 = input_array[2*i+1];
+                coord_type_ext tmp2_idx = index_array[2*i+1];
+                coord_type_ext tmp3;
+                centre_index_type tmp3_idx;
+                if (tmp1 < tmp2) {
+                    tmp3 = tmp1;
+                    tmp3_idx = tmp1_idx;
+                } else {
+                    tmp3 = tmp2;
+                    tmp3_idx = tmp2_idx;
+                }
+
+                input_array[i] = tmp3;
+                index_array[i] = (tmp3_idx);
+            }
+            if (m > uint(m/(1<<(j+1)))*(1<<(j+1)) ) {
+                input_array[uint(m/(1<<(j+1)))] = (input_array[uint(m/(1<<(j+1))-1)*2+2]);
+                index_array[uint(m/(1<<(j+1)))] = (index_array[uint(m/(1<<(j+1))-1)*2+2]);
+            }
+        }
+        if (j== ceil(log2(m))-1) {
+            coord_type_ext tmp1 = input_array[0];
+            coord_type_ext tmp1_idx = index_array[0];
+            coord_type_ext tmp2 = input_array[1];
+            coord_type_ext tmp2_idx = index_array[1];
+            coord_type_ext tmp3;
+            centre_index_type tmp3_idx;
+            if (tmp1 < tmp2) {
+                tmp3 = tmp1;
+                tmp3_idx = tmp1_idx;
+            } else {
+                tmp3 = tmp2;
+                tmp3_idx = tmp2_idx;
+            }
+            input_array[0] = (tmp3);
+            index_array[0] = (tmp3_idx);
+        }
+    }
+    *res_val= input_array[0];
+    *res_idx= index_array[0];
+}
+
+
+// compute the Euclidean distance
+void compute_distance(data_type p1, data_type p2, coord_type_ext *dist)
+{
+    #pragma HLS inline
+
+    data_type tmp_p1 = p1;
+    data_type tmp_p2 = p2;
+    coord_type_ext tmp_mul_res[D];
+
+    for (uint d=0; d<D; d++) {
+        #pragma HLS unroll
+        coord_type tmp_sub1 = get_coord_type_vector_item(tmp_p1.value,d);
+        coord_type tmp_sub2 = get_coord_type_vector_item(tmp_p2.value,d);
+        coord_type tmp = tmp_sub1 - tmp_sub2;
+        coord_type_ext tmp_ext = (coord_type_ext)tmp;
+        coord_type_ext tmp_mul = fi_mul(tmp_ext,tmp_ext);
+        tmp_mul_res[d] = tmp_mul;
+        #pragma HLS resource variable=tmp core=AddSubnS
+        //#pragma HLS resource variable=tmp_mul core=MulnS
+    }
+
+    *dist = tree_adder(tmp_mul_res,D);
+}
+
+
+
+
diff --git a/benchmarks/kmeans/lloyds_algorithm_util.h b/benchmarks/kmeans/lloyds_algorithm_util.h
new file mode 100644
index 0000000..3853c3d
--- /dev/null
+++ b/benchmarks/kmeans/lloyds_algorithm_util.h
@@ -0,0 +1,33 @@
+/**********************************************************************
+* Felix Winterstein, Imperial College London
+*
+* File: lloyds_algorithm_util.h
+*
+* Revision 1.01
+* Additional Comments: distributed under a BSD license, see LICENSE.txt
+*
+**********************************************************************/
+
+#ifndef LLOYDS_ALGORITHM_UTIL_H
+#define LLOYDS_ALGORITHM_UTIL_H
+
+#include "lloyds_algorithm_top.h"
+
+
+// helper functions
+void set_coord_type_vector_item(coord_type_vector *a, const coord_type b, const uint idx);
+void set_coord_type_vector_ext_item(coord_type_vector_ext *a, const coord_type_ext b, const uint idx);
+coord_type get_coord_type_vector_item(const coord_type_vector a, const uint idx);
+coord_type_ext get_coord_type_vector_ext_item(const coord_type_vector_ext a, const uint idx);
+
+data_type conv_long_to_short(data_type_ext p);
+data_type_ext conv_short_to_long(data_type p);
+mul_input_type saturate_mul_input(coord_type_ext val);
+coord_type_ext fi_mul(coord_type_ext op1, coord_type_ext op2);
+coord_type_ext tree_adder(coord_type_ext *input_array);
+void tree_cs(coord_type_ext *input_array,centre_index_type *index_array,coord_type_ext *res_val,centre_index_type *res_idx,const uint m);
+coord_type_ext tree_adder(coord_type_ext *input_array,const uint m);
+void compute_distance(data_type p1, data_type p2, coord_type_ext *dist);
+
+
+#endif  /* LLOYDS_ALGORITHM_UTIL_H */