diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
@@ -1213,6 +1213,89 @@
                 - !ScalarExpression
                   scalar_arg: K
 --- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+  name: conv_1d_ncw_fcw
+  cpp_class_name: Conv1DNcwFcwOp
+  doc: |-
+    Performs 1-D convolution.
+    Numeric casting is performed on the operands to the inner multiply, promoting
+    them to the same data type as the accumulator/output.
+  implements:
+  - LinalgConvolutionOpInterface
+structured_op: !LinalgStructuredOpConfig
+  args:
+  - !LinalgOperandDefConfig
+    name: I
+    kind: input_tensor
+    type_var: T1
+    shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6] -> (s0, s1, s2 * s3 + s4
+      * s5)>
+  - !LinalgOperandDefConfig
+    name: K
+    kind: input_tensor
+    type_var: T2
+    shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6] -> (s6, s1, s4)>
+  - !LinalgOperandDefConfig
+    name: O
+    kind: output_tensor
+    type_var: U
+    shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6] -> (s0, s6, s2)>
+  - !LinalgOperandDefConfig
+    name: strides
+    kind: index_attr
+    index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6] -> (s3)>
+    default_indices:
+    - 1
+  - !LinalgOperandDefConfig
+    name: dilations
+    kind: index_attr
+    index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6] -> (s5)>
+    default_indices:
+    - 1
+  indexing_maps: !LinalgIndexingMapsConfig
+    static_indexing_maps:
+    - affine_map<(d0, d1, d2, d3, d4)[s0, s1, s2, s3, s4, s5, s6] -> (d0, d4, d1 *
+      s3 + d3 * s5)>
+    - affine_map<(d0, d1, d2, d3, d4)[s0, s1, s2, s3, s4, s5, s6] -> (d2, d4, d3)>
+    - affine_map<(d0, d1, d2, d3, d4)[s0, s1, s2, s3, s4, s5, s6] -> (d0, d2, d1)>
+  iterator_types:
+  - parallel
+  - parallel
+  - parallel
+  - reduction
+  - reduction
+  assignments:
+  - !ScalarAssign
+    arg: O
+    value: !ScalarExpression
+      scalar_fn:
+        kind: binary
+        fn_name: add
+        operands:
+        - !ScalarExpression
+          scalar_arg: O
+        - !ScalarExpression
+          scalar_fn:
+            kind: binary
+            fn_name: mul
+            operands:
+            - !ScalarExpression
+              scalar_fn:
+                kind: type
+                fn_name: cast_signed
+                type_var: U
+                operands:
+                - !ScalarExpression
+                  scalar_arg: I
+            - !ScalarExpression
+              scalar_fn:
+                kind: type
+                fn_name: cast_signed
+                type_var: U
+                operands:
+                - !ScalarExpression
+                  scalar_arg: K
+--- !LinalgOpConfig
 metadata: !LinalgOpMetadata
   name: conv_2d_nhwc_hwcf
   cpp_class_name: Conv2DNhwcHwcfOp
diff --git a/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py b/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
--- a/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
+++ b/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
@@ -269,6 +269,21 @@
       U, I[D.n, D.ow * S.SW + D.kw * S.DW, D.c]) * TypeFn.cast_signed(
           U, K[D.kw, D.c, D.f])
 
+@linalg_structured_op
+def conv_1d_ncw_fcw(I=TensorDef(T1, S.N, S.C, S.OW * S.SW + S.KW * S.DW),
+                    K=TensorDef(T2, S.F, S.C, S.KW),
+                    O=TensorDef(U, S.N, S.F, S.OW, output=True),
+                    strides=IndexAttrDef(S.SW, default=[1]),
+                    dilations=IndexAttrDef(S.DW, default=[1])):
+  """Performs 1-D convolution.
+  Numeric casting is performed on the operands to the inner multiply, promoting
+  them to the same data type as the accumulator/output.
+  """
+  implements(ConvolutionOpInterface)
+  domain(D.n, D.ow, D.f, D.kw, D.c)
+  O[D.n, D.f, D.ow] += TypeFn.cast_signed(
+      U, I[D.n, D.c, D.ow * S.SW + D.kw * S.DW]) * TypeFn.cast_signed(
+          U, K[D.f, D.c, D.kw])
 
 @linalg_structured_op
 def conv_2d_nhwc_hwcf(I=TensorDef(T1, S.N, S.OH * S.SH + S.KH * S.DH,
diff --git a/mlir/test/Dialect/Linalg/generalize-named-ops.mlir b/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
--- a/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
+++ b/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
@@ -178,6 +178,32 @@
 
 // -----
 
+func.func @conv_1d_ncw_fcw(%input: memref<?x?x?xf32>, %filter: memref<?x?x?xf32>, %output: memref<?x?x?xf32>) {
+  linalg.conv_1d_ncw_fcw {dilations = dense<1> : tensor<1xi64>,
+                                       strides = dense<1> : tensor<1xi64>}
+     ins (%input, %filter: memref<?x?x?xf32>, memref<?x?x?xf32>)
+    outs (%output: memref<?x?x?xf32>)
+  return
+}
+// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d4, d1 + d3)>
+// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d2, d4, d3)>
+// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d2, d1)>
+
+// CHECK: func @conv_1d_ncw_fcw
+
+// CHECK: linalg.generic
+// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]
+// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction", "reduction"]}
+// CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<?x?x?xf32>, memref<?x?x?xf32>)
+// CHECK-SAME: outs(%{{.+}} : memref<?x?x?xf32>)
+
+// CHECK:         ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32)
+// CHECK-NEXT:      %[[MUL:.+]] = arith.mulf %[[BBARG0]], %[[BBARG1]] : f32
+// CHECK-NEXT:      %[[ADD:.+]] = arith.addf %[[BBARG2]], %[[MUL]] : f32
+// CHECK-NEXT:      linalg.yield %[[ADD]] : f32
+
+// -----
+
 func.func @generalize_fill(%output: memref<?x?xf32>, %value : f32) {
   linalg.fill ins(%value : f32) outs(%output : memref<?x?xf32>)
   return
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
@@ -243,6 +243,38 @@
 
 // -----
 
+// CHECK-LABEL: func @conv_1d_ncw_fcw
+func.func @conv_1d_ncw_fcw(%input: tensor<?x?x?xf32>, %filter: tensor<?x?x?xf32>, %init: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
+  // CHECK:      %{{.+}} = linalg.conv_1d_ncw_fcw
+  // CHECK-SAME:   dilations = dense<1> : tensor<1xi64>
+  // CHECK-SAME:   strides = dense<1> : tensor<1xi64>
+  // CHECK-SAME:   ins(%{{.+}}, %{{.+}} : tensor<?x?x?xf32>, tensor<?x?x?xf32>)
+  // CHECK-SAME:   outs(%{{.+}} : tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
+  %0 = linalg.conv_1d_ncw_fcw {dilations = dense<1> : tensor<1xi64>,
+                                            strides = dense<1> : tensor<1xi64>}
+     ins (%input, %filter: tensor<?x?x?xf32>, tensor<?x?x?xf32>)
+    outs (%init: tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
+  return %0 : tensor<?x?x?xf32>
+}
+
+// -----
+
+// CHECK-LABEL: func @conv_1d_ncw_fcw
+func.func @conv_1d_ncw_fcw(%input: memref<?x?x?xf32>, %filter: memref<?x?x?xf32>, %output: memref<?x?x?xf32>) {
+  // CHECK:      linalg.conv_1d_ncw_fcw
+  // CHECK-SAME:   dilations = dense<1> : tensor<1xi64>
+  // CHECK-SAME:   strides = dense<1> : tensor<1xi64>
+  // CHECK-SAME:   ins(%{{.+}}, %{{.+}} : memref<?x?x?xf32>, memref<?x?x?xf32>)
+  // CHECK-SAME:   outs(%{{.+}} : memref<?x?x?xf32>)
+  linalg.conv_1d_ncw_fcw {dilations = dense<1> : tensor<1xi64>,
+                                       strides = dense<1> : tensor<1xi64>}
+     ins (%input, %filter: memref<?x?x?xf32>, memref<?x?x?xf32>)
+    outs (%output: memref<?x?x?xf32>)
+  return
+}
+
+// -----
+
 // CHECK-LABEL: func @conv_2d_nhwc_hwcf
 func.func @conv_2d_nhwc_hwcf(%input: tensor<?x?x?x?xf32>, %filter: tensor<?x?x?x?xf32>, %init: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
   // CHECK:      %{{.+}} = linalg.conv_2d_nhwc_hwcf