diff --git a/src/runtime/NEON/functions/NEGEMM.cpp b/src/runtime/NEON/functions/NEGEMM.cpp
index baa22b7..be96445 100644
--- a/src/runtime/NEON/functions/NEGEMM.cpp
+++ b/src/runtime/NEON/functions/NEGEMM.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -175,7 +175,7 @@
     const bool is_c_bias = gemm_info.reshape_b_only_on_first_run();
 
     ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(a);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::BFLOAT16, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(0) != b->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
diff --git a/src/runtime/NEON/functions/NEGEMMAssemblyDispatch.cpp b/src/runtime/NEON/functions/NEGEMMAssemblyDispatch.cpp
index 91b91d66..a3080e7 100644
--- a/src/runtime/NEON/functions/NEGEMMAssemblyDispatch.cpp
+++ b/src/runtime/NEON/functions/NEGEMMAssemblyDispatch.cpp
@@ -505,17 +505,17 @@
 
 Status NEGEMMAssemblyDispatch::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *d, const GEMMInfo &gemm_info)
 {
-    ARM_COMPUTE_UNUSED(gemm_info);
-    ARM_COMPUTE_UNUSED(c);
+    ARM_COMPUTE_UNUSED(gemm_info, c);
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(a, b, d);
     ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(a);
+    ARM_COMPUTE_RETURN_ERROR_ON_CPU_BF16_UNSUPPORTED(a);
 #ifndef __aarch64__
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->element_size() == 1, "8bit integer types only supported for aarch64");
 #endif /* __aarch64__ */
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S8,
-                                                         DataType::F16, DataType::F32);
+                                                         DataType::BFLOAT16, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(b, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL, DataType::S8,
-                                                         DataType::F16, DataType::F32);
+                                                         DataType::BFLOAT16, DataType::F16, DataType::F32);
     if(is_data_type_quantized_per_channel(b->data_type()))
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QASYMM8_SIGNED, DataType::S8);
@@ -526,6 +526,7 @@
     }
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->data_type() == DataType::F32 && d->data_type() != DataType::F32, "Only F32 output supported for F32 input");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->data_type() == DataType::F16 && d->data_type() != DataType::F16, "Only F16 output supported for F16 input");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->data_type() == DataType::BFLOAT16 && d->data_type() != DataType::F32, "Only F32 output supported for BFLOAT16 input");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->data_type() == DataType::U8 && d->data_type() != DataType::U32, "Only U32 output supported for U8 input");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->data_type() == DataType::S8 && d->data_type() != DataType::S32, "Only S32 output supported for S8 input");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->data_type() == DataType::QASYMM8 && d->data_type() != DataType::QASYMM8, "Only QASYMM8 output supported for QASYMM8 input");
@@ -578,6 +579,11 @@
             }
             break;
 #endif /* __aarch64__ */
+#if defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16)
+        case DataType::BFLOAT16:
+            create_arm_gemm<bfloat16, float>(_arm_gemm, _memory_group, a, b, c, d, act, gemm_info, _weights_manager);
+            break;
+#endif /* defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16) */
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
         case DataType::F16:
             create_arm_gemm<float16_t, float16_t>(_arm_gemm, _memory_group, a, b, c, d, act, gemm_info, _weights_manager);
diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
index 5701d60..f6dc3a8 100644
--- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -62,7 +62,7 @@
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1,
                                                          DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL,
-                                                         DataType::F16, DataType::F32);
+                                                         DataType::BFLOAT16, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
 
     if(biases != nullptr)
@@ -330,6 +330,7 @@
     }
 
     // Create temporary GEMM output tensor in case we cannot skip col2im
+    const DataType output_data_type = data_type == DataType::BFLOAT16 ? DataType::F32 : data_type;
     if(!_skip_col2im)
     {
         TensorShape shape_gemm;
@@ -340,7 +341,7 @@
         shape_gemm.set(1, conv_w * conv_h);
 
         // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
-        TensorInfo info_gemm(shape_gemm, 1, data_type);
+        TensorInfo info_gemm(shape_gemm, 1, output_data_type);
         info_gemm.set_quantization_info(output->info()->quantization_info()).set_data_layout(input->info()->data_layout());
         _gemm_output.allocator()->init(info_gemm);
         _memory_group.manage(&_gemm_output);
@@ -392,8 +393,8 @@
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!");
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::BFLOAT16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL, DataType::BFLOAT16, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(num_groups > 1, "Grouping (num_groups != 1) is not supported on NEON");
 
@@ -497,16 +498,17 @@
     }
 
     // Create temporary GEMM output tensor in case we cannot skip col2im
+    const DataType output_data_type = data_type == DataType::BFLOAT16 ? DataType::F32 : data_type;
     if(!skip_col2im)
     {
         TensorShape shape_gemm = gemm_input_to_use->tensor_shape();
         shape_gemm.set(0, mat_weights_cols);
         shape_gemm.set(1, conv_w * conv_h);
-        info_gemm = TensorInfo(shape_gemm, 1, data_type);
+        info_gemm = TensorInfo(shape_gemm, 1, output_data_type);
     }
     else
     {
-        info_gemm = TensorInfo(output->tensor_shape(), 1, data_type);
+        info_gemm = TensorInfo(output->tensor_shape(), 1, output_data_type);
     }
     info_gemm.set_quantization_info(output->quantization_info()).set_data_layout(input->data_layout());
     gemm_output_to_use = &info_gemm;