diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOpsSpec.tc b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOpsSpec.tc
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOpsSpec.tc
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOpsSpec.tc
@@ -352,6 +352,18 @@
                                        ow * strides[1] + kw * dilations[1], c));
 }
 
+ods_def<PoolingNDHWCSumFOp>:
+def pooling_ndhwc_sum
+    (I: f32(N, D, H, W, C), K: f32(KD, KH, KW))
+  -> (O: f32(N, OD, OH, OW, C))
+  attr(strides: 3xi64, dilations: 3xi64)
+{
+  O(n, od, oh, ow, c) = AddFOp<kd, kh, kw>(
+      O(n, od, oh, ow, c), I(n, od * strides[0] + kd * dilations[0],
+                             oh * strides[1] + kh * dilations[1],
+                             ow * strides[2] + kw * dilations[2], c));
+}
+
 ods_def<PoolingNHWCMaxI8Op>:
 def pooling_nhwc_i8_max
     (I: i8(N, H, W, C), K: i8(KH, KW))
@@ -412,6 +424,23 @@
                          O(n, oh, ow, c));
 }
 
+ods_def<PoolingNDHWCMaxFOp>:
+def pooling_ndhwc_max
+    (I: f32(N, D, H, W, C), K: f32(KD, KH, KW))
+  -> (O: f32(N, OD, OH, OW, C))
+  attr(strides: 3xi64, dilations: 3xi64)
+{
+  O(n, od, oh, ow, c) =
+      SelectOp<kd, kh, kw>(CmpFOpOGT(I(n, od * strides[0] + kd * dilations[0],
+                                       oh * strides[1] + kh * dilations[1],
+                                       ow * strides[2] + kw * dilations[2], c),
+                                     O(n, od, oh, ow, c)),
+                           I(n, od * strides[0] + kd * dilations[0],
+                             oh * strides[1] + kh * dilations[1],
+                             ow * strides[2] + kw * dilations[2], c),
+                           O(n, od, oh, ow, c));
+}
+
 ods_def<PoolingNHWCMinFOp>:
 def pooling_nhwc_min
     (I: f32(N, H, W, C), K: f32(KH, KW))
@@ -426,3 +455,20 @@
                            ow * strides[1] + kw * dilations[1], c),
                          O(n, oh, ow, c));
 }
+
+ods_def<PoolingNDHWCMinFOp>:
+def pooling_ndhwc_min
+    (I: f32(N, D, H, W, C), K: f32(KD, KH, KW))
+  -> (O: f32(N, OD, OH, OW, C))
+  attr(strides: 3xi64, dilations: 3xi64)
+{
+  O(n, od, oh, ow, c) =
+      SelectOp<kd, kh, kw>(CmpFOpOLT(I(n, od * strides[0] + kd * dilations[0],
+                                       oh * strides[1] + kh * dilations[1],
+                                       ow * strides[2] + kw * dilations[2], c),
+                                     O(n, od, oh, ow, c)),
+                           I(n, od * strides[0] + kd * dilations[0],
+                             oh * strides[1] + kh * dilations[1],
+                             ow * strides[2] + kw * dilations[2], c),
+                           O(n, od, oh, ow, c));
+}
diff --git a/mlir/test/Dialect/Linalg/named-ops.mlir b/mlir/test/Dialect/Linalg/named-ops.mlir
--- a/mlir/test/Dialect/Linalg/named-ops.mlir
+++ b/mlir/test/Dialect/Linalg/named-ops.mlir
@@ -518,3 +518,105 @@
     outs(%output: memref<1x2x2x1xf32>)
   return
 }
+
+// -----
+
+// CHECK-LABEL: func @pooling_ndhwc_sum_tensor
+// CHECK:         %{{.+}} = linalg.pooling_ndhwc_sum
+// CHECK-SAME:      dilations = dense<1> : tensor<3xi64>
+// CHECK-SAME:      strides = dense<1> : tensor<3xi64>
+// CHECK-SAME:      ins(%{{.+}}, %{{.+}} : tensor<1x4x4x4x1xf32>, tensor<3x3x3xf32>)
+// CHECK-SAME:      outs(%{{.+}} : tensor<1x2x2x2x1xf32>) -> tensor<1x2x2x2x1xf32>
+func @pooling_ndhwc_sum_tensor(%input: tensor<1x4x4x4x1xf32>) -> tensor<1x2x2x2x1xf32> {
+  %fake = linalg.init_tensor [3, 3, 3] : tensor<3x3x3xf32>
+  %init = linalg.init_tensor [1, 2, 2, 2, 1] : tensor<1x2x2x2x1xf32>
+  %cst = constant 0.000000e+00 : f32
+  %fill = linalg.fill(%cst, %init) : f32, tensor<1x2x2x2x1xf32> -> tensor<1x2x2x2x1xf32>
+  %res = linalg.pooling_ndhwc_sum {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}
+    ins(%input, %fake: tensor<1x4x4x4x1xf32>, tensor<3x3x3xf32>)
+    outs(%fill: tensor<1x2x2x2x1xf32>) -> tensor<1x2x2x2x1xf32>
+  return %res : tensor<1x2x2x2x1xf32>
+}
+
+// -----
+
+// CHECK-LABEL: func @pooling_ndhwc_sum
+// CHECK:         linalg.pooling_ndhwc_sum
+// CHECK-SAME:      dilations = dense<1> : tensor<3xi64>
+// CHECK-SAME:      strides = dense<1> : tensor<3xi64>
+// CHECK-SAME:      ins(%{{.+}}, %{{.+}} : memref<1x4x4x4x1xf32>, memref<3x3x3xf32>)
+// CHECK-SAME:      outs(%{{.+}} : memref<1x2x2x2x1xf32>)
+func @pooling_ndhwc_sum(%input: memref<1x4x4x4x1xf32>, %fake: memref<3x3x3xf32>, %output: memref<1x2x2x2x1xf32>) {
+  linalg.pooling_ndhwc_sum {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}
+    ins(%input, %fake: memref<1x4x4x4x1xf32>, memref<3x3x3xf32>)
+    outs(%output: memref<1x2x2x2x1xf32>)
+  return
+}
+
+// -----
+
+// CHECK-LABEL: func @pooling_ndhwc_max_tensor
+// CHECK:         %{{.+}} = linalg.pooling_ndhwc_max
+// CHECK-SAME:      dilations = dense<1> : tensor<3xi64>
+// CHECK-SAME:      strides = dense<1> : tensor<3xi64>
+// CHECK-SAME:      ins(%{{.+}}, %{{.+}} : tensor<1x4x4x4x1xf32>, tensor<3x3x3xf32>)
+// CHECK-SAME:      outs(%{{.+}} : tensor<1x2x2x2x1xf32>) -> tensor<1x2x2x2x1xf32>
+func @pooling_ndhwc_max_tensor(%input: tensor<1x4x4x4x1xf32>) -> tensor<1x2x2x2x1xf32> {
+  %fake = linalg.init_tensor [3, 3, 3] : tensor<3x3x3xf32>
+  %init = linalg.init_tensor [1, 2, 2, 2, 1] : tensor<1x2x2x2x1xf32>
+  %cst = constant 0.000000e+00 : f32
+  %fill = linalg.fill(%cst, %init) : f32, tensor<1x2x2x2x1xf32> -> tensor<1x2x2x2x1xf32>
+  %res = linalg.pooling_ndhwc_max {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}
+    ins(%input, %fake: tensor<1x4x4x4x1xf32>, tensor<3x3x3xf32>)
+    outs(%fill: tensor<1x2x2x2x1xf32>) -> tensor<1x2x2x2x1xf32>
+  return %res : tensor<1x2x2x2x1xf32>
+}
+
+// -----
+
+// CHECK-LABEL: func @pooling_ndhwc_max
+// CHECK:         linalg.pooling_ndhwc_max
+// CHECK-SAME:      dilations = dense<1> : tensor<3xi64>
+// CHECK-SAME:      strides = dense<1> : tensor<3xi64>
+// CHECK-SAME:      ins(%{{.+}}, %{{.+}} : memref<1x4x4x4x1xf32>, memref<3x3x3xf32>)
+// CHECK-SAME:      outs(%{{.+}} : memref<1x2x2x2x1xf32>)
+func @pooling_ndhwc_max(%input: memref<1x4x4x4x1xf32>, %fake: memref<3x3x3xf32>, %output: memref<1x2x2x2x1xf32>) {
+  linalg.pooling_ndhwc_max {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}
+    ins(%input, %fake: memref<1x4x4x4x1xf32>, memref<3x3x3xf32>)
+    outs(%output: memref<1x2x2x2x1xf32>)
+  return
+}
+
+// -----
+
+// CHECK-LABEL: func @pooling_ndhwc_min_tensor
+// CHECK:         %{{.+}} = linalg.pooling_ndhwc_min
+// CHECK-SAME:      dilations = dense<1> : tensor<3xi64>
+// CHECK-SAME:      strides = dense<1> : tensor<3xi64>
+// CHECK-SAME:      ins(%{{.+}}, %{{.+}} : tensor<1x4x4x4x1xf32>, tensor<3x3x3xf32>)
+// CHECK-SAME:      outs(%{{.+}} : tensor<1x2x2x2x1xf32>) -> tensor<1x2x2x2x1xf32>
+func @pooling_ndhwc_min_tensor(%input: tensor<1x4x4x4x1xf32>) -> tensor<1x2x2x2x1xf32> {
+  %fake = linalg.init_tensor [3, 3, 3] : tensor<3x3x3xf32>
+  %init = linalg.init_tensor [1, 2, 2, 2, 1] : tensor<1x2x2x2x1xf32>
+  %cst = constant 0.000000e+00 : f32
+  %fill = linalg.fill(%cst, %init) : f32, tensor<1x2x2x2x1xf32> -> tensor<1x2x2x2x1xf32>
+  %res = linalg.pooling_ndhwc_min {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}
+    ins(%input, %fake: tensor<1x4x4x4x1xf32>, tensor<3x3x3xf32>)
+    outs(%fill: tensor<1x2x2x2x1xf32>) -> tensor<1x2x2x2x1xf32>
+  return %res : tensor<1x2x2x2x1xf32>
+}
+
+// -----
+
+// CHECK-LABEL: func @pooling_ndhwc_min
+// CHECK:         linalg.pooling_ndhwc_min
+// CHECK-SAME:      dilations = dense<1> : tensor<3xi64>
+// CHECK-SAME:      strides = dense<1> : tensor<3xi64>
+// CHECK-SAME:      ins(%{{.+}}, %{{.+}} : memref<1x4x4x4x1xf32>, memref<3x3x3xf32>)
+// CHECK-SAME:      outs(%{{.+}} : memref<1x2x2x2x1xf32>)
+func @pooling_ndhwc_min(%input: memref<1x4x4x4x1xf32>, %fake: memref<3x3x3xf32>, %output: memref<1x2x2x2x1xf32>) {
+  linalg.pooling_ndhwc_min {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}
+    ins(%input, %fake: memref<1x4x4x4x1xf32>, memref<3x3x3xf32>)
+    outs(%output: memref<1x2x2x2x1xf32>)
+  return
+}