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) ceil(Dim/DV)
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`¶

editDistance¶

#include "xf_data_analytics/text/editDistance.hpp"

template <
    int N,
    int M,
    int BITS
    >
void editDistance (
    hls::stream <ap_uint <BITS>>& len1_strm,
    hls::stream <ap_uint <64>>& query_strm,
    hls::stream <ap_uint <BITS>>& len2_strm,
    hls::stream <ap_uint <64>>& input_strm,
    hls::stream <ap_uint <BITS>>& max_ed_strm,
    hls::stream <bool>& i_e_strm,
    hls::stream <bool>& o_e_strm,
    hls::stream <bool>& o_match_strm
    )

Levenshtein distance implementation.

Parameters:

N	maximum length of query string.
M	maximum length of input stream string, N must be less than M.
BITS	data width of internal edit distance in bits.
len1_strm	length of the query string in bytes.
query_strm	the query string folded into mutiple 8B elements.
len2_strm	length of each input string in bytes.
input_strm	input strings to compute edit distance against the given query string, which is folded into multiple 8B elements.
max_ed_strm	the maximum threshold of edit distance.
i_e_strm	end flag of input_strm and max_ed_strm.
o_e_strm	end flag of output matched stream.
o_match_strm	only the calculated ED less than threshold in max_ed_strm will be TRUE.

regexVM¶

#include "xf_data_analytics/text/regexVM.hpp"

template <int STACK_SIZE>
void regexVM (
    ap_uint <32>* bitSetBuff,
    ap_uint <64>* instrBuff,
    ap_uint <32>* msgBuff,
    unsigned int lenMsg,
    ap_uint <2>& match,
    ap_uint <16>* offsetBuff
    )

Implementation for regular expression VM (1 instruction per iteration).

Parameters:

STACK_SIZE	Size of internal stack.
bitSetBuff	Bit set map for character class.
instrBuff	Instruction buffer.
msgBuff	Message buffer as input string.
lenMsg	Length of input string.
match	Flag to indicate whether the input string is matched or not, 0 for mismatch, 1 for match, 2 for stack overflow.
offsetBuff	Offset address for each capturing group.

regexVM_opt¶

#include "xf_data_analytics/text/regexVM.hpp"

template <int STACK_SIZE>
void regexVM_opt (
    ap_uint <32>* bitSetBuff,
    ap_uint <64>* instrBuff,
    ap_uint <32>* msgBuff,
    unsigned int lenMsg,
    ap_uint <2>& match,
    ap_uint <16>* offsetBuff
    )

Implementation for regular expression VM (2 instructions per iteration).

Parameters:

STACK_SIZE	Size of internal stack.
bitSetBuff	Bit set map for cclass.
instrBuff	Instruction buffer.
msgBuff	Message buffer as input string.
lenMsg	Length of input string.
match	Flag to indicate whether the input string is matched or not, 0 for mismatch, 1 for match, 2 for stack overflow.
offsetBuff	Offset address for each capturing group.

Hardware Functions in `xf::data_analytics::dataframe`¶

csvParser¶

#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

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.

Hardware Functions in xf::data_analytics::classification¶

decisionTreePredict¶

axiVarColToStreams¶

naiveBayesTrain¶

naiveBayesPredict¶

svmPredict¶

Hardware Functions in xf::data_analytics::clustering¶

kMeansPredict¶

Hardware Functions in xf::data_analytics::regression¶

decisionTreePredict¶

Hardware Functions in xf::data_analytics::text¶

editDistance¶

regexVM¶

regexVM_opt¶

Hardware Functions in xf::data_analytics::dataframe¶

csvParser¶

jsonParser¶

readFromDataFrame¶

writeToDataFrame¶

Software C Functions¶

xf_re_compile¶

Hardware Functions in `xf::data_analytics::classification`¶

Hardware Functions in `xf::data_analytics::clustering`¶

Hardware Functions in `xf::data_analytics::regression`¶

Hardware Functions in `xf::data_analytics::text`¶

Hardware Functions in `xf::data_analytics::dataframe`¶