The DATAFLOW directive is applied to the top function. User could specify the individual sub-function implementiontations using a configuration class derived from the following basic class by redefining the appropriate class member:
template <int RowsColsA, typename InputType, typename OutputType>
struct qrInverseTraits {
typedef float InternalType;
typedef qrfTraits QRF_CONFIG;
typedef backSubstituteTraits<RowsColsA, InternalType, InternalType> BACK_SUB_CONFIG;
typedef matrixMultiplyTraits<NoTranspose,
NoTranspose,
RowsColsA,
RowsColsA,
RowsColsA,
RowsColsA,
InternalType,
OutputType>
MULTIPLIER_CONFIG;
};
The configuration class is supplied to the xf::solver::qrInverse function as a template paramter as follows. The sub-functions are executed sequentially: QRF, back substitution, and matrix multiply. The implementation selected for these sub-functions determines the resource utilization and function throughput/latency of the Inverse function.
template <int RowsColsA,
typename InputType,
typename OutputType,
typename QRInverseTraits = qrInverseTraits<RowsColsA, InputType, OutputType> >
void qrInverse(hls::stream<InputType>& matrixAStrm, hls::stream<OutputType>& matrixInverseAStrm, int& A_singular) {
#pragma HLS DATAFLOW
// Define intermediate buffers
hls::stream<typename QRInverseTraits::InternalType> matrixQStrm;
#pragma HLS STREAM variable = matrixQStrm depth = 16
hls::stream<typename QRInverseTraits::InternalType> matrixRStrm;
#pragma HLS STREAM variable = matrixRStrm depth = 16
hls::stream<typename QRInverseTraits::InternalType> matrixInverseRStrm;
#pragma HLS STREAM variable = matrixInverseRStrm depth = 16
// Run QR factorization, get upper-triangular result in R, orthogonal/unitary matrix Q
const bool TRANSPOSED_Q = true; // Q is produced in transpose form such that Q*A = R
qrf<TRANSPOSED_Q, RowsColsA, RowsColsA, InputType, typename QRInverseTraits::InternalType, typename QRInverseTraits::QRF_CONFIG>(matrixAStrm, matrixQStrm, matrixRStrm);
// Run back-substitution to compute R^-1
backSubstitute<RowsColsA, typename QRInverseTraits::InternalType, typename QRInverseTraits::InternalType, typename QRInverseTraits::BACK_SUB_CONFIG>(matrixRStrm, matrixInverseRStrm, A_singular);
// A^-1 = R^-1*Qt
matrixMultiply<NoTranspose, NoTranspose, RowsColsA, RowsColsA, RowsColsA, RowsColsA, RowsColsA, RowsColsA, typename QRInverseTraits::InternalType, OutputType, typename QRInverseTraits::MULTIPLIER_CONFIG>(matrixInverseRStrm, matrixQStrm, matrixInverseAStrm);
}