Hardware Functions in xf::data_analytics::classification¶
decisionTreePredict¶
#include "xf_data_analytics/classification/decision_tree_predict.hpp"
template < typename MType, unsigned int WD, unsigned int MAX_FEA_NUM, unsigned int MAX_TREE_DEPTH = 20, unsigned MAX_CAT_BITS = 8 > void decisionTreePredict ( hls::stream <ap_uint <WD>> dstrm_batch [MAX_FEA_NUM], hls::stream <bool>& estrm_batch, hls::stream <ap_uint <512>>& treeStrm, hls::stream <bool>& treeTag, hls::stream <ap_uint <MAX_CAT_BITS>>& predictionsStrm, hls::stream <bool>& predictionsTag )
decisionTreePredict, Top function of Decision Tree Predict.
This function first loads decision tree (the corresponding function : getTree) from treeStrm Then, read sample one by one from dstrm_batch, and output its category id into predictionsStrm streams
Note that the treeStrm is a 512-bit stream, and each 512 bits include two nodes. In each 512-bit confirm the range(0,71) is node[i].nodeInfo and range(256,327) is node[i+1].nodeInfo the range(192,255) is node[i].threshold and range(448,511) is node[i+1].threshold For detailed info of Node struct, can refer “decision_tree.hpp” Samples in input sample stream should be converted into ap_uint<WD> from MType
Parameters:
| MType | The data type of sample |
| WD | The width of data type MType, can get by sizeof(MType) |
| MAX_FEA_NUM | The max feature num function can support |
| MAX_TREE_DEPTH | The max tree depth function can support |
| MAX_CAT_BITS | The category max bit number |
| dstrm_batch | Input data streams of ap_uint<WD> |
| estrm_batch | End flag stream for input data |
| treeStrm | Decision tree streams |
| treeTag | End flag stream for decision tree nodes |
| predictionsStrm | Output data streams |
| predictionsTagStrm | End flag stream for output |
axiVarColToStreams¶
#include "xf_data_analytics/classification/decision_tree_train.hpp"
template < int _BurstLen = 32, int _WAxi = 512, int _WData = 64 > void axiVarColToStreams ( ap_uint <_WAxi>* ddr, const ap_uint <32> offset, const ap_uint <32> rows, ap_uint <32> cols, hls::stream <ap_uint <_WData>> dataStrm [_WAxi/_WData], hls::stream <bool>& eDataStrm )
Loading table from AXI master to stream. Table should be row based storage of identical datawidth.
Parameters:
| _BurstLen | burst length of AXI buffer, default is 32. |
| _WAxi | width of AXI port, must be multiple of datawidth, default is 512. |
| _WData | datawith, default is 64. |
| ddr | input AXI port |
| offset | offset(in _WAxi bits) to load table. |
| rows | Row number of table |
| cols | Column number of table Output streams of _WAxi/_WData channels end flag of output stream. |
naiveBayesTrain¶
#include "xf_data_analytics/classification/naive_bayes.hpp"
template < int DT_WIDTH = 32, int WL = 3, typename DT = unsigned int > void naiveBayesTrain ( const int num_of_class, const int num_of_term, hls::stream <ap_uint <64>> i_data_strm [1<< WL], hls::stream <bool> i_e_strm [1<< WL], hls::stream <int>& o_terms_strm, hls::stream <ap_uint <64>> o_data0_strm [1<< WL], hls::stream <ap_uint <64>> o_data1_strm [1<< WL] )
naiveBayesTrain, top function of multinomial Naive Bayes Training.
This function will firstly load train dataset from the i_data_strm, then counte the frequency for each hit term. After scaning all sample, the likehood probability matrix and prior probability will be output from two independent stream
Parameters:
| DT_WIDTH | the width of type DT, in bits |
| WL | the width of bit to enable dispatcher, only 3 is supported so far |
| DT | the data type of internal counter for terms, can be 32/64-bit integer, float or double |
| num_of_class | the number of class in sample dataset, should be exactly same with real dataset |
| num_of_term | the number of terms, must be larger than the number of feature, and num_of_class * num_of_term <= (1 << (20-WL)) must be satisfied. |
| i_data_strm | input data stream of ap_uint<64> in multiple channel |
| i_e_strm | end flag stream for each input data channel |
| o_terms_strm | the output number of statistic feature |
| o_data0_strm | the output likehood matrix |
| o_data1_strm | the output prior probablity vector |
naiveBayesPredict¶
#include "xf_data_analytics/classification/naive_bayes.hpp"
template < int CH_NM, int GRP_NM > void naiveBayesPredict ( const int num_of_class, const int num_of_term, hls::stream <ap_uint <64>>& i_theta_strm, hls::stream <ap_uint <64>>& i_prior_strm, hls::stream <ap_uint <32>> i_data_strm [CH_NM], hls::stream <bool>& i_e_strm, hls::stream <ap_uint <10>>& o_class_strm, hls::stream <bool>& o_e_strm )
naiveBayesPredict, top function of multinomial Naive Bayes Prediction
The function will firstly load the train model into on-chip memory, and calculate the classfication results for each sample using argmax function.
Parameters:
| CH_NM | the number of channel for input sample data, should be power of 2 |
| GRP_NM | the unroll factor for handling the classes simultaneously, must be power of 2 in 1~256 |
| num_of_term | the number of class, should be exactly same with the input dataset |
| num_of_term | the number of feature, should be exactly same with the input dataset |
| i_theta_strm | the input likehood probability stream, [num_of_class][num_of_term] |
| i_prior_strm | the input prior probability stream, [num_of_class] |
| i_data_strm | the input of test data stream |
| i_e_strm | end flag stream for i_data_strm |
| o_class_strm | the prediction result for each input sample |
| o_e_strm | end flag stream for o_class_strm |
svmPredict¶
#include "xf_data_analytics/classification/svm_predict.hpp"
template < typename MType, unsigned WD, unsigned StreamN, unsigned SampleDepth > void svmPredict ( const int cols, hls::stream <MType> sample_strm [StreamN], hls::stream <bool>& e_sample_strm, hls::stream <ap_uint <512>>& weight_strm, hls::stream <bool>& eTag, hls::stream <ap_uint <1>>& predictionsStrm, hls::stream <bool>& predictionsTag )
svmPredict, Top function of svm Predict.
This function first loads weight (the corresponding function : getWeight) from weight_strm Then, read sample from sample_strm, and output its classification id into predictionsStrm streams
Parameters:
| MType | The data type of sample |
| WD | The width of data type MType, can get by sizeof(MType) |
| StreamN | The stream number of input sample stream vector |
| SampleDepth | stream depth number of one input sample |
| cols | colum number of input data sample |
| sample_strm | Input data streams of MType |
| e_sample_strm | End flag stream for input data |
| weight_strm | weight streams |
| eTag | End flag stream for weight streams |
| predictionsStrm | Output data streams |
| predictionsTagStrm | End flag stream for output |
Hardware Functions in xf::data_analytics::clustering¶
kMeansPredict¶
#include "xf_data_analytics/clustering/kmeansPredict.hpp"
template < typename DT, int Dim, int Kcluster, int uramDepth, int KU, int DV > void kMeansPredict ( hls::stream <ap_uint <sizeof (DT)*8>> sampleStrm [DV], hls::stream <bool>& endSampleStrm, ap_uint <sizeof (DT)*8*DV> centers [KU][uramDepth], const int dims, const int kcluster, hls::stream <ap_uint <32>>& tagStrm, hls::stream <bool>& endTagStrm )
kMeansPredict predicts cluster index for each sample. In order to achive to acceleration, please make sure partition 1-dim of centers.
Parameters:
| DT | data type, supporting float and double |
| Dim | the maximum number of dimensions,dynamic number of dimension should be not greater than the maximum. |
| Kcluster | the maximum number of cluster,dynamic number of cluster should be not greater than the maximum. |
| uramDepth | the depth of uram where centers are stored. uramDepth should be not less than ceiling(Kcluster/KU)
|
| KU | unroll factor of Kcluster, KU centers are took part in calculating distances concurrently with one sample. After Kcluster/KU+1 times at most, ouput the minimum distance of a sample and Kcluster centers. |
| DV | unroll factor of Dim, DV elements in a center are took part in calculating distances concurrently with one sample. |
| sampleStrm | input sample streams, a sample needs ceiling(dims/DV) times to read. |
| endSampleStrm | the end flag of sample stream. |
| centers | an array stored centers, user should partition dim=1 in its defination. |
| dims | the number of dimensions. |
| kcluster | the number of clusters. |
| tagStrm | tag stream, label a cluster ID for each sample. |
| endTagStrm | end flag of tag stream. |
Hardware Functions in xf::data_analytics::regression¶
decisionTreePredict¶
#include "xf_data_analytics/regression/decision_tree_predict.hpp"
template < typename MType, unsigned int WD, unsigned int MAX_FEA_NUM, unsigned int MAX_TREE_DEPTH = 10 > void decisionTreePredict ( hls::stream <ap_uint <WD>> dstrm_batch [MAX_FEA_NUM], hls::stream <bool>& estrm_batch, hls::stream <ap_uint <512>>& treeStrm, hls::stream <bool>& treeTag, hls::stream <MType>& predictionsStrm, hls::stream <bool>& predictionsTag )
decisionTreePredict, Top function of Decision Tree Predict.
This function first loads decision tree (the corresponding function : getTree) from treeStrm Then, read sample one by one from dstrm_batch, and output its category id into predictionsStrm streams
Note that the treeStrm is a 512-bit stream, and each 512 bits include two nodes. In each 512-bit confirm the range(0,71) is node[i].nodeInfo and range(256,327) is node[i+1].nodeInfo the range(72,135) is node[i].regValue and range(328,391) is node[i+1].regValue the range(192,255) is node[i].threshold and range(448,511) is node[i+1].threshold For detailed info of NodeR struct, can refer “decision_tree_L1.hpp” Samples in input sample stream should be converted into ap_uint<WD> from MType
Parameters:
| MType | The data type of sample |
| WD | The width of data type MType, can get by sizeof(MType) |
| MAX_FEA_NUM | The max feature num function can support |
| MAX_TREE_DEPTH | The max tree depth function can support |
| dstrm_batch | Input data streams of ap_uint<WD> |
| estrm_batch | End flag stream for input data |
| treeStrm | Decision tree streams |
| treeTag | End flag stream for decision tree nodes |
| predictionsStrm | Output regression value streams |
| predictionsTagStrm | End flag stream for output |
Hardware Functions in xf::data_analytics::text¶
Hardware Functions in xf::data_analytics::dataframe¶
csvParser¶
csvParser overload (1)¶
#include "xf_data_analytics/dataframe/csv_parser.hpp"
template <int PU_NUM = 8> void csvParser ( ap_uint <128>* csv_buf, ap_uint <8>* schema, hls::stream <Object>& o_obj_strm )
read one standard CSV file from DDR and parse into object stream with schma defination
Parameters:
| PU_NUM | number of CSV parse core, only support 2/4/8 |
| csv_buf | buffer of CSV file |
| schema | name, data type and is_filter flag for each column |
| o_obj_strm | output object stream for selected columns |
csvParser overload (2)¶
#include "xf_data_analytics/dataframe/csv_parser_v2.hpp"
template <int PU_NUM = 8> void csvParser ( ap_uint <128>* csv_buf, ap_uint <8>* schema, ap_uint <32> line_cnt_buf [PU_NUM/2][2], hls::stream <Object>& o_obj_strm )
read one standard CSV file from DDR and parse into object stream with schma defination
Parameters:
| PU_NUM | number of CSV parse core, only support 2/4/8 |
| csv_buf | buffer of CSV file |
| schema | name, data type and is_filter flag for each column |
| line_cnt_buf | output num of csv lines that each pu processes. |
| o_obj_strm | output object stream for selected columns |
jsonParser¶
#include "xf_data_analytics/dataframe/json_parser.hpp"
template < int PU_NUM = 4, int COL_NUM = 16 > void jsonParser ( ap_uint <128>* json_buf, ap_uint <8>* schema, hls::stream <Object>& o_obj_strm )
read one standard JSON file from DDR and parse into object stream with schma defination
Parameters:
| PU_NUM | number of JSON parse core, only support 2/4/8 |
| COL_NUM | number of maximum column, should be power of 2 |
| json_buf | buffer of JSON file |
| schema | name, data type and is_filter flag for each column |
| o_obj_strm | output object stream for selected columns |
readFromDataFrame¶
#include "xf_data_analytics/dataframe/read_from_dataframe.hpp"
void readFromDataFrame ( int field_type [17], ap_uint <64>* ddr_buff, hls::stream <Object>& obj_strm )
read the data frame format data from DDR and pack into object streams
Parameters:
| field_type | the data type of each field id (may obtained from schema) |
| ddr_buff | the ddr that saves data |
| obj_strm | output Object stream |
writeToDataFrame¶
#include "xf_data_analytics/dataframe/write_to_dataframe.hpp"
void writeToDataFrame ( hls::stream <Object>& obj_strm, ap_uint <64>* ddr_buff )
write object stream data to DDR
all field_id in each json line is unique maximum supported field_id number is 16
Parameters:
| obj_strm | input stream data with type Object Struct |
| ddr_buff | DDR buffer to save parserd data in DataFrame format |
Software C Functions¶
xf_re_compile¶
#include "xf_data_analytics/text/xf_re_compile.h"
int xf_re_compile ( const char* pattern, unsigned int* bitset, uint64_t* instructions, unsigned int* instr_num, unsigned int* cclass_num, unsigned int* cpgp_nm, uint8_t* cpgp_name_val, uint32_t* cpgp_name_oft )
Software compiler for pre-compiling input regular expression.
Parameters:
| pattern | Input regular expression. |
| bitset | Bit set map for each character class. |
| instructions | Compiled instruction list derived from input pattern. |
| instr_num | Number of instructions. |
| cclass_num | Number of character classes. |
| cpgp_nm | Number of capturing groups. |
| cpgp_name_val | Buffer of every name of each capturing group. |
| cpgp_name_oft | Starting offset addresses for the name of each capturing group. |