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diff --git a/debian/ti-timl/usr/share/ti/examples/timl/app/cnn/interop/caffe/appCNNInteropCaffeConvLayerConvert.cpp b/debian/ti-timl/usr/share/ti/examples/timl/app/cnn/interop/caffe/appCNNInteropCaffeConvLayerConvert.cpp
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+++ b/debian/ti-timl/usr/share/ti/examples/timl/app/cnn/interop/caffe/appCNNInteropCaffeConvLayerConvert.cpp
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+/******************************************************************************/
+/*!
+ * \file appCNNInteropCaffeConvLayerConvert.cpp
+ */
+/* Copyright (C) 2015 Texas Instruments Incorporated - http://www.ti.com/
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ *    Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ *
+ *    Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the
+ *    distribution.
+ *
+ *    Neither the name of Texas Instruments Incorporated nor the names of
+ *    its contributors may be used to endorse or promote products derived
+ *    from this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ ******************************************************************************/
+
+
+/*******************************************************************************
+ *
+ *  INCLUDES
+ *
+ ******************************************************************************/
+
+#include "appCNNInteropCaffe.hpp"
+
+
+/******************************************************************************/
+/*!
+ * \ingroup   appCNNInteropCaffe
+ * \brief     Convert Caffe conv layer
+ * \param[in] cnn CNN
+ * \param[in] layerStructure Layer structure
+ * \param[in] layerParam     Layer params
+ * \return    Error code
+ */
+/******************************************************************************/
+
+int appCNNInteropCaffeConvLayerConvert(timlConvNeuralNetwork *cnn, LayerParameter layerStructure, LayerParameter layerParam)
+{
+   float             *kernel;
+   int               pad;
+   int               stride;
+   int               kernelSize;
+   int               channel;
+   int               group;
+   int               size;
+   timlCNNLayer      *layer;
+   timlCNNConvParams params;
+
+   pad        = layerStructure.convolution_param().pad();
+   stride     = layerStructure.convolution_param().stride();
+   kernelSize = layerStructure.convolution_param().kernel_size();
+   channel    = layerStructure.convolution_param().num_output();
+   group      = layerStructure.convolution_param().group();
+
+   params          = timlCNNConvParamsDefault();
+   params.padUp    = pad;
+   params.padDown  = pad;
+   params.padLeft  = pad;
+   params.padRight = pad;
+   params.type     = Util_Corr2D;
+
+   // add conv layer
+   timlCNNAddConvLayer(cnn, kernelSize, kernelSize, stride, stride, channel, params);
+   layer = cnn->tail;
+   timlCNNConvInitialize(layer);
+
+   // read kernel
+   size = layerParam.blobs(0).data_size();
+   kernel = (float*)malloc(sizeof(float)*size);
+   for (int i = 0; i < size; i++) {
+      kernel[i] = layerParam.blobs(0).data(i);
+   }
+
+   // convert grouped kernel
+   appCNNInteropCaffeFillBlockDiagonalMatrix(layer->convParams.kernel, layer->convParams.outputFeatureMapChannel, layer->convParams.inputFeatureMapChannel*layer->convParams.kernelRow*layer->convParams.kernelCol, group, kernel);
+   free(kernel);
+
+//   // flip all the kernels
+//   if (layer->convParams.type  == Util_Conv2D) {
+//      appCNNInteropCaffeFlipKernelMatrix(layer->convParams.kernel, kernelSize, kernelSize, layer->convParams.inputFeatureMapChannel, layer->convParams.outputFeatureMapChannel);
+//   }
+
+   // read bias
+   size = layerParam.blobs(1).data_size();
+   for (int i = 0; i < size; i++) {
+      layer->convParams.bias[i] = layerParam.blobs(1).data(i);
+   }
+
+   return 0;
+}