diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu
index 03c9ab79..135a2ea9 100644
--- a/src/convolutional_kernels.cu
+++ b/src/convolutional_kernels.cu
@@ -74,6 +74,38 @@ void binarize_weights_gpu(float *weights, int n, int size, float *binary)
     check_error(cudaPeekAtLastError());
 }
 
+__global__ void cuda_f32_to_f16(float* input_f32, size_t size, half *output_f16)
+{
+	int idx = blockIdx.x * blockDim.x + threadIdx.x;
+	if (idx < size) output_f16[idx] = input_f32[idx];
+}
+
+void cuda_convert_f32_to_f16(float* input_f32, size_t size, half *output_f16) {
+	cuda_f32_to_f16 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> (input_f32, size, output_f16);
+}
+
+__global__ void cuda_f16_to_f32(half* input_f16, size_t size, float *output_f32)
+{
+	int idx = blockIdx.x * blockDim.x + threadIdx.x;
+	if (idx < size) output_f32[idx] = input_f16[idx];
+}
+
+void cuda_convert_f16_to_f32(half* input_f16, size_t size, float *output_f32) {
+	cuda_f16_to_f32 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> (input_f16, size, output_f32);
+}
+
+half *cuda_make_f16_from_f32_array(float *src, size_t n)
+{
+	half *dst16;
+	size_t size = sizeof(half)*n;
+	check_error(cudaMalloc((void **)&dst16, size));
+	if (src) {
+		cuda_convert_f32_to_f16(src, n, dst16);
+	}
+	if (!dst16) error("Cuda malloc failed\n");
+	return dst16;
+}
+
 void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
 {
     fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1);
@@ -90,9 +122,57 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
     }
 
 #ifdef CUDNN
-    float one = 1;
+	//float one = 1;	// alpha[0], beta[0] is float for HALF and FLOAT
+	float alpha = 1, beta = 0;
+
+#ifdef CUDNN_HALF
+	// Note: For improved performance it is advised to use beta[0] = 0.0. 
+	// For Tensor Core: cudnnSetConvolutionMathType() where cudnnMathType_t mathType = CUDNN_TENSOR_OP_MATH;
+	// 1. or CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM and use CUDNN_DATA_HALF
+	// 2. or CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED
+	// More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops
+
+	const size_t input16_size = l.batch*l.c*l.w*l.h;
+	static size_t max_input16_size = input16_size;
+	static half* input16 = cuda_make_f16_from_f32_array(NULL, max_input16_size);
+
+	const size_t output16_size = l.batch*l.out_c*l.out_h*l.out_w;
+	static size_t max_output16_size = output16_size;
+	static half* output16 = cuda_make_f16_from_f32_array(NULL, max_output16_size);
+
+	if (max_input16_size < input16_size) {
+		max_input16_size = input16_size;
+		cuda_free((float *)input16);
+		input16 = cuda_make_f16_from_f32_array(state.input, max_input16_size);
+	}
+
+	if (max_output16_size < output16_size) {
+		max_output16_size = output16_size;
+		cuda_free((float *)output16);
+		output16 = cuda_make_f16_from_f32_array(NULL, max_output16_size);
+	}
+
+	cuda_convert_f32_to_f16(state.input, input16_size, input16);
+
+	cudnnConvolutionForward(cudnn_handle(),
+		&alpha,
+		l.srcTensorDesc,
+		input16,
+		l.weightDesc,
+		l.weights_gpu16,
+		l.convDesc,
+		l.fw_algo,
+		state.workspace,
+		l.workspace_size,
+		&beta,
+		l.dstTensorDesc,
+		output16);
+	
+	cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu);
+#else
+
     cudnnConvolutionForward(cudnn_handle(),
-                &one,
+                &alpha,
                 l.srcTensorDesc,
                 state.input,
                 l.weightDesc,
@@ -101,9 +181,11 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
                 l.fw_algo,
                 state.workspace,
                 l.workspace_size,
-                &one,
+                &beta,
                 l.dstTensorDesc,
                 l.output_gpu);
+#endif
+
 
 #else
     int i;
@@ -232,6 +314,9 @@ void pull_convolutional_layer(convolutional_layer layer)
 void push_convolutional_layer(convolutional_layer layer)
 {
     cuda_push_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size);
+#ifdef CUDNN_HALF
+	cuda_convert_f32_to_f16(layer.weights_gpu, layer.c*layer.n*layer.size*layer.size, (half *)layer.weights_gpu16);
+#endif
     cuda_push_array(layer.biases_gpu, layer.biases, layer.n);
     cuda_push_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size);
     cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 801270ab..68658086 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -139,22 +139,38 @@ size_t get_workspace_size(layer l){
 #ifdef CUDNN
 void cudnn_convolutional_setup(layer *l, int cudnn_preference)
 {
-    cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w); 
-    cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); 
-    cudnnSetFilter4dDescriptor(l->dweightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); 
 
-    cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w); 
-    cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); 
-    cudnnSetFilter4dDescriptor(l->weightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); 
+#ifdef CUDNN_HALF
+	// TRUE_HALF_CONFIG is only supported on architectures with true fp16 support (compute capability 5.3 and 6.0):
+	// Tegra X1, Jetson TX1, DRIVE CX, DRIVE PX, Quadro GP100, Tesla P100
+	const cudnnDataType_t data_type = CUDNN_DATA_HALF;
+#else
+	cudnnDataType_t data_type = CUDNN_DATA_FLOAT;
+#endif
+	// Tensor Core uses CUDNN_TENSOR_OP_MATH instead of CUDNN_DEFAULT_MATH
+	cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH);
+
+	// INT8_CONFIG, INT8_EXT_CONFIG, INT8x4_CONFIG and INT8x4_EXT_CONFIG are only supported 
+	// on architectures with DP4A support (compute capability 6.1 and later).
+	//cudnnDataType_t data_type = CUDNN_DATA_INT8;
+
+    cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->c, l->h, l->w);
+    cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w);
+    cudnnSetFilter4dDescriptor(l->dweightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size);
+
+    cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->c, l->h, l->w);
+    cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w);
+    cudnnSetFilter4dDescriptor(l->weightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size);
 #if(CUDNN_MAJOR >= 6)
-	cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT);	// cudnn 6.0
+	cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, data_type);	// cudnn >= 6.0
 #else
 	cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION);	// cudnn 5.1
 #endif
 	int forward_algo = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST;
 	int backward_algo = CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST;
 	int backward_filter = CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST;
-	if (cudnn_preference == cudnn_smallest) {
+	if (cudnn_preference == cudnn_smallest) 
+	{
 		forward_algo = CUDNN_CONVOLUTION_FWD_NO_WORKSPACE;
 		backward_algo = CUDNN_CONVOLUTION_BWD_DATA_NO_WORKSPACE;
 		backward_filter = CUDNN_CONVOLUTION_BWD_FILTER_NO_WORKSPACE;
@@ -275,6 +291,9 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
         }
 
         l.weights_gpu = cuda_make_array(l.weights, c*n*size*size);
+#ifdef CUDNN_HALF
+		l.weights_gpu16 = cuda_make_array(l.weights, c*n*size*size/2);
+#endif
         l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size);
 
         l.biases_gpu = cuda_make_array(l.biases, n);
diff --git a/src/layer.h b/src/layer.h
index db012f1f..0f5addac 100644
--- a/src/layer.h
+++ b/src/layer.h
@@ -242,6 +242,8 @@ struct layer{
     float * weights_gpu;
     float * weight_updates_gpu;
 
+	float * weights_gpu16;
+
     float * biases_gpu;
     float * bias_updates_gpu;