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diff --git a/runtimes/nn/depend/external/eigen/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h b/runtimes/nn/depend/external/eigen/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
deleted file mode 100644
index 492eb0a29..000000000
--- a/runtimes/nn/depend/external/eigen/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
+++ /dev/null
@@ -1,345 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
-#define EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, typename ResultType>
-static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res, bool sortedInsertion = false)
-{
-  typedef typename remove_all<Lhs>::type::Scalar Scalar;
-
-  // make sure to call innerSize/outerSize since we fake the storage order.
-  Index rows = lhs.innerSize();
-  Index cols = rhs.outerSize();
-  eigen_assert(lhs.outerSize() == rhs.innerSize());
-  
-  ei_declare_aligned_stack_constructed_variable(bool,   mask,     rows, 0);
-  ei_declare_aligned_stack_constructed_variable(Scalar, values,   rows, 0);
-  ei_declare_aligned_stack_constructed_variable(Index,  indices,  rows, 0);
-  
-  std::memset(mask,0,sizeof(bool)*rows);
-
-  evaluator<Lhs> lhsEval(lhs);
-  evaluator<Rhs> rhsEval(rhs);
-  
-  // estimate the number of non zero entries
-  // given a rhs column containing Y non zeros, we assume that the respective Y columns
-  // of the lhs differs in average of one non zeros, thus the number of non zeros for
-  // the product of a rhs column with the lhs is X+Y where X is the average number of non zero
-  // per column of the lhs.
-  // Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs)
-  Index estimated_nnz_prod = lhsEval.nonZerosEstimate() + rhsEval.nonZerosEstimate();
-
-  res.setZero();
-  res.reserve(Index(estimated_nnz_prod));
-  // we compute each column of the result, one after the other
-  for (Index j=0; j<cols; ++j)
-  {
-
-    res.startVec(j);
-    Index nnz = 0;
-    for (typename evaluator<Rhs>::InnerIterator rhsIt(rhsEval, j); rhsIt; ++rhsIt)
-    {
-      Scalar y = rhsIt.value();
-      Index k = rhsIt.index();
-      for (typename evaluator<Lhs>::InnerIterator lhsIt(lhsEval, k); lhsIt; ++lhsIt)
-      {
-        Index i = lhsIt.index();
-        Scalar x = lhsIt.value();
-        if(!mask[i])
-        {
-          mask[i] = true;
-          values[i] = x * y;
-          indices[nnz] = i;
-          ++nnz;
-        }
-        else
-          values[i] += x * y;
-      }
-    }
-    if(!sortedInsertion)
-    {
-      // unordered insertion
-      for(Index k=0; k<nnz; ++k)
-      {
-        Index i = indices[k];
-        res.insertBackByOuterInnerUnordered(j,i) = values[i];
-        mask[i] = false;
-      }
-    }
-    else
-    {
-      // alternative ordered insertion code:
-      const Index t200 = rows/11; // 11 == (log2(200)*1.39)
-      const Index t = (rows*100)/139;
-
-      // FIXME reserve nnz non zeros
-      // FIXME implement faster sorting algorithms for very small nnz
-      // if the result is sparse enough => use a quick sort
-      // otherwise => loop through the entire vector
-      // In order to avoid to perform an expensive log2 when the
-      // result is clearly very sparse we use a linear bound up to 200.
-      if((nnz<200 && nnz<t200) || nnz * numext::log2(int(nnz)) < t)
-      {
-        if(nnz>1) std::sort(indices,indices+nnz);
-        for(Index k=0; k<nnz; ++k)
-        {
-          Index i = indices[k];
-          res.insertBackByOuterInner(j,i) = values[i];
-          mask[i] = false;
-        }
-      }
-      else
-      {
-        // dense path
-        for(Index i=0; i<rows; ++i)
-        {
-          if(mask[i])
-          {
-            mask[i] = false;
-            res.insertBackByOuterInner(j,i) = values[i];
-          }
-        }
-      }
-    }
-  }
-  res.finalize();
-}
-
-
-} // end namespace internal
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, typename ResultType,
-  int LhsStorageOrder = (traits<Lhs>::Flags&RowMajorBit) ? RowMajor : ColMajor,
-  int RhsStorageOrder = (traits<Rhs>::Flags&RowMajorBit) ? RowMajor : ColMajor,
-  int ResStorageOrder = (traits<ResultType>::Flags&RowMajorBit) ? RowMajor : ColMajor>
-struct conservative_sparse_sparse_product_selector;
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
-{
-  typedef typename remove_all<Lhs>::type LhsCleaned;
-  typedef typename LhsCleaned::Scalar Scalar;
-
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorMatrix;
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorMatrixAux;
-    typedef typename sparse_eval<ColMajorMatrixAux,ResultType::RowsAtCompileTime,ResultType::ColsAtCompileTime,ColMajorMatrixAux::Flags>::type ColMajorMatrix;
-    
-    // If the result is tall and thin (in the extreme case a column vector)
-    // then it is faster to sort the coefficients inplace instead of transposing twice.
-    // FIXME, the following heuristic is probably not very good.
-    if(lhs.rows()>rhs.cols())
-    {
-      ColMajorMatrix resCol(lhs.rows(),rhs.cols());
-      // perform sorted insertion
-      internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol, true);
-      res = resCol.markAsRValue();
-    }
-    else
-    {
-      ColMajorMatrixAux resCol(lhs.rows(),rhs.cols());
-      // ressort to transpose to sort the entries
-      internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrixAux>(lhs, rhs, resCol, false);
-      RowMajorMatrix resRow(resCol);
-      res = resRow.markAsRValue();
-    }
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-     typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorMatrix;
-     RowMajorMatrix rhsRow = rhs;
-     RowMajorMatrix resRow(lhs.rows(), rhs.cols());
-     internal::conservative_sparse_sparse_product_impl<RowMajorMatrix,Lhs,RowMajorMatrix>(rhsRow, lhs, resRow);
-     res = resRow;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorMatrix;
-    RowMajorMatrix lhsRow = lhs;
-    RowMajorMatrix resRow(lhs.rows(), rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<Rhs,RowMajorMatrix,RowMajorMatrix>(rhs, lhsRow, resRow);
-    res = resRow;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorMatrix;
-    RowMajorMatrix resRow(lhs.rows(), rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<Rhs,Lhs,RowMajorMatrix>(rhs, lhs, resRow);
-    res = resRow;
-  }
-};
-
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
-{
-  typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
-
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorMatrix;
-    ColMajorMatrix resCol(lhs.rows(), rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol);
-    res = resCol;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorMatrix;
-    ColMajorMatrix lhsCol = lhs;
-    ColMajorMatrix resCol(lhs.rows(), rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<ColMajorMatrix,Rhs,ColMajorMatrix>(lhsCol, rhs, resCol);
-    res = resCol;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorMatrix;
-    ColMajorMatrix rhsCol = rhs;
-    ColMajorMatrix resCol(lhs.rows(), rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<Lhs,ColMajorMatrix,ColMajorMatrix>(lhs, rhsCol, resCol);
-    res = resCol;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorMatrix;
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorMatrix;
-    RowMajorMatrix resRow(lhs.rows(),rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<Rhs,Lhs,RowMajorMatrix>(rhs, lhs, resRow);
-    // sort the non zeros:
-    ColMajorMatrix resCol(resRow);
-    res = resCol;
-  }
-};
-
-} // end namespace internal
-
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, typename ResultType>
-static void sparse_sparse_to_dense_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-{
-  typedef typename remove_all<Lhs>::type::Scalar Scalar;
-  Index cols = rhs.outerSize();
-  eigen_assert(lhs.outerSize() == rhs.innerSize());
-
-  evaluator<Lhs> lhsEval(lhs);
-  evaluator<Rhs> rhsEval(rhs);
-
-  for (Index j=0; j<cols; ++j)
-  {
-    for (typename evaluator<Rhs>::InnerIterator rhsIt(rhsEval, j); rhsIt; ++rhsIt)
-    {
-      Scalar y = rhsIt.value();
-      Index k = rhsIt.index();
-      for (typename evaluator<Lhs>::InnerIterator lhsIt(lhsEval, k); lhsIt; ++lhsIt)
-      {
-        Index i = lhsIt.index();
-        Scalar x = lhsIt.value();
-        res.coeffRef(i,j) += x * y;
-      }
-    }
-  }
-}
-
-
-} // end namespace internal
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, typename ResultType,
-  int LhsStorageOrder = (traits<Lhs>::Flags&RowMajorBit) ? RowMajor : ColMajor,
-  int RhsStorageOrder = (traits<Rhs>::Flags&RowMajorBit) ? RowMajor : ColMajor>
-struct sparse_sparse_to_dense_product_selector;
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_to_dense_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    internal::sparse_sparse_to_dense_product_impl<Lhs,Rhs,ResultType>(lhs, rhs, res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_to_dense_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorMatrix;
-    ColMajorMatrix lhsCol(lhs);
-    internal::sparse_sparse_to_dense_product_impl<ColMajorMatrix,Rhs,ResultType>(lhsCol, rhs, res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_to_dense_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorMatrix;
-    ColMajorMatrix rhsCol(rhs);
-    internal::sparse_sparse_to_dense_product_impl<Lhs,ColMajorMatrix,ResultType>(lhs, rhsCol, res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_to_dense_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    Transpose<ResultType> trRes(res);
-    internal::sparse_sparse_to_dense_product_impl<Rhs,Lhs,Transpose<ResultType> >(rhs, lhs, trRes);
-  }
-};
-
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H