diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaMakeBroadcastable.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaMakeBroadcastable.cpp
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaMakeBroadcastable.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaMakeBroadcastable.cpp
@@ -86,17 +86,18 @@
   auto input1Ty = input1.getType().dyn_cast<RankedTensorType>();
   auto input2Ty = input2.getType().dyn_cast<RankedTensorType>();
 
-  if (!input1Ty || !input2Ty)
-    return failure();
+  if (!input1Ty || !input2Ty) {
+    return rewriter.notifyMatchFailure(loc, "input not a ranked tensor");
+  }
 
   int64_t input1Rank = input1Ty.getRank();
   int64_t input2Rank = input2Ty.getRank();
 
-  Value higherTensorValue, lowerTensorValue;
-  // Cannot rewrite as its already correct.
   if (input1Rank == input2Rank)
-    return failure();
+    return rewriter.notifyMatchFailure(loc,
+                                       "cannot rewrite as its already correct");
 
+  Value higherTensorValue, lowerTensorValue;
   if (input1Rank > input2Rank) {
     higherTensorValue = input1;
     lowerTensorValue = input2;
@@ -107,7 +108,6 @@
 
   ArrayRef<int64_t> higherRankShape =
       higherTensorValue.getType().cast<RankedTensorType>().getShape();
-  (void)higherRankShape;
   ArrayRef<int64_t> lowerRankShape =
       lowerTensorValue.getType().cast<RankedTensorType>().getShape();
 
@@ -115,7 +115,7 @@
 
   if (computeReshapeOutput(higherRankShape, lowerRankShape, reshapeOutputShape)
           .failed())
-    return failure();
+    return rewriter.notifyMatchFailure(loc, "fail to compute a reshape type");
 
   auto reshapeInputType = lowerTensorValue.getType().cast<RankedTensorType>();
   auto reshapeOutputType = RankedTensorType::get(
@@ -125,7 +125,8 @@
   if (outputType) {
     if (outputType.getShape().size() != reshapeOutputShape.size() ||
         outputType.getShape().size() != higherRankShape.size())
-      return failure();
+      return rewriter.notifyMatchFailure(
+          loc, "the reshaped type doesn't agrees with the ranked output type");
   }
 
   auto reshapeLower = rewriter.create<tosa::ReshapeOp>(
@@ -144,7 +145,8 @@
 }
 
 namespace {
-template <typename OpTy> struct ConvertTosaOp : public OpRewritePattern<OpTy> {
+template <typename OpTy>
+struct ConvertTosaOp : public OpRewritePattern<OpTy> {
   using OpRewritePattern<OpTy>::OpRewritePattern;
 
   LogicalResult matchAndRewrite(OpTy tosaBinaryOp,
@@ -232,6 +234,60 @@
     return success();
   }
 };
+
+template <>
+struct ConvertTosaOp<tosa::SelectOp> : public OpRewritePattern<tosa::SelectOp> {
+  using OpRewritePattern<tosa::SelectOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tosa::SelectOp tosaOp,
+                                PatternRewriter &rewriter) const override {
+
+    Value input1 = tosaOp.getPred();
+    Value input2 = tosaOp.getOnTrue();
+    Value input3 = tosaOp.getOnFalse();
+    Value output = tosaOp.getResult();
+
+    auto outputType = output.getType().dyn_cast<RankedTensorType>();
+    if (!outputType)
+      return rewriter.notifyMatchFailure(tosaOp, "output not a ranked tensor");
+
+    Value result1 = input1;
+    Value result2 = input2;
+    Value result3 = input3;
+
+    // Apply broadcasting to each pair of inputs separately, and chain them as
+    // compound as below so that the broadcasting happens all at once.
+    bool reshaped1 = reshapeLowerToHigher(rewriter, tosaOp.getLoc(), outputType,
+                                          input1, input2, result1, result2)
+                         .succeeded();
+
+    bool reshaped2 = reshapeLowerToHigher(rewriter, tosaOp.getLoc(), outputType,
+                                          result1, input3, result1, result3)
+                         .succeeded();
+
+    bool reshaped3 = reshapeLowerToHigher(rewriter, tosaOp.getLoc(), outputType,
+                                          result2, input3, result2, result3)
+                         .succeeded();
+
+    if (!reshaped1 && !reshaped2 && !reshaped3)
+      return rewriter.notifyMatchFailure(
+          tosaOp,
+          "cannot rewrite as the rank of all operands is already aligned");
+
+    int32_t result1Rank = result1.getType().cast<RankedTensorType>().getRank();
+    int32_t result2Rank = result2.getType().cast<RankedTensorType>().getRank();
+    int32_t result3Rank = result3.getType().cast<RankedTensorType>().getRank();
+
+    if ((result1Rank != result2Rank) || (result2Rank != result3Rank))
+      return rewriter.notifyMatchFailure(
+          tosaOp, "not all ranks are aligned with each other");
+
+    rewriter.replaceOpWithNewOp<tosa::SelectOp>(tosaOp, outputType, result1,
+                                                result2, result3);
+
+    return success();
+  }
+};
 } // namespace
 
 namespace {
@@ -263,6 +319,7 @@
     patterns.add<ConvertTosaOp<tosa::LogicalAndOp>>(ctx);
     patterns.add<ConvertTosaOp<tosa::LogicalOrOp>>(ctx);
     patterns.add<ConvertTosaOp<tosa::LogicalXorOp>>(ctx);
+    patterns.add<ConvertTosaOp<tosa::SelectOp>>(ctx);
     patterns.add<ConvertTosaOp<tosa::PowOp>>(ctx);
     (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
   }
diff --git a/mlir/test/Dialect/Tosa/broadcast.mlir b/mlir/test/Dialect/Tosa/broadcast.mlir
--- a/mlir/test/Dialect/Tosa/broadcast.mlir
+++ b/mlir/test/Dialect/Tosa/broadcast.mlir
@@ -195,3 +195,91 @@
   %0 = "tosa.add"(%arg0, %arg1) : (tensor<i32>, tensor<17x16x15x14xi32>) -> tensor<17x16x15x14xi32>
   return %0 : tensor<17x16x15x14xi32>
 }
+
+// -----
+// CHECK-LABEL: broadcast_select_both_input
+func.func @test_broadcast_select_both_input(%arg0: tensor<1x16x16xi1>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<1x16x16xf32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.reshape"(%arg1) {new_shape = array<i64: 1, 1, 1>}
+  // CHECK-DAG: %[[VAL_1:.*]] = "tosa.reshape"(%arg2) {new_shape = array<i64: 1, 1, 1>}
+  // CHECK: %[[VAL_2:.*]] = "tosa.select"(%arg0, %[[VAL_0]], %[[VAL_1]])
+  %0 = "tosa.select"(%arg0, %arg1, %arg2) : (tensor<1x16x16xi1>, tensor<f32>, tensor<f32>) -> tensor<1x16x16xf32>
+  return %0 : tensor<1x16x16xf32>
+}
+
+// -----
+// CHECK-LABEL: broadcast_select_one_input
+func.func @test_broadcast_select_one_input(%arg0: tensor<17x16x15x14xi1>, %arg1: tensor<17x16x15x14xf32>, %arg2: tensor<f32>) -> tensor<17x16x15x14xf32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.reshape"(%arg2) {new_shape = array<i64: 1, 1, 1, 1>}
+  // CHECK: %[[VAL_1:.*]] = "tosa.select"(%arg0, %arg1, %[[VAL_0]])
+  %0 = "tosa.select"(%arg0, %arg1, %arg2) : (tensor<17x16x15x14xi1>, tensor<17x16x15x14xf32>, tensor<f32>) -> tensor<17x16x15x14xf32>
+  return %0 : tensor<17x16x15x14xf32>
+}
+
+// -----
+// CHECK-LABEL: broadcast_select_predicate
+func.func @test_broadcast_select_predicate(%arg0: tensor<i1>, %arg1: tensor<1x32x32x8xf32>, %arg2: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.reshape"(%arg0) {new_shape = array<i64: 1, 1, 1, 1>}
+  // CHECK: %[[VAL_1:.*]] = "tosa.select"(%[[VAL_0]], %arg1, %arg2)
+  %0 = "tosa.select"(%arg0, %arg1, %arg2) : (tensor<i1>, tensor<1x32x32x8xf32>, tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32>
+  return %0 : tensor<1x32x32x8xf32>
+}
+
+// -----
+// CHECK-LABEL: broadcast_select_abc
+func.func @test_broadcast_select_abc(%arg0: tensor<i1>, %arg1: tensor<32x8xf32>, %arg2: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.reshape"(%arg0) {new_shape = array<i64: 1, 1, 1, 1>}
+  // CHECK-DAG: %[[VAL_1:.*]] = "tosa.reshape"(%arg1) {new_shape = array<i64: 1, 1, 32, 8>}
+  // CHECK: %[[VAL_2:.*]] = "tosa.select"(%[[VAL_0]], %[[VAL_1]], %arg2)
+  %0 = "tosa.select"(%arg0, %arg1, %arg2) : (tensor<i1>, tensor<32x8xf32>, tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32>
+  return %0 : tensor<1x32x32x8xf32>
+}
+
+// -----
+// CHECK-LABEL: broadcast_select_acb
+func.func @test_broadcast_select_acb(%arg0: tensor<i1>, %arg1: tensor<1x32x32x8xf32>, %arg2: tensor<32x8xf32>) -> tensor<1x32x32x8xf32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.reshape"(%arg0) {new_shape = array<i64: 1, 1, 1, 1>}
+  // CHECK-DAG: %[[VAL_1:.*]] = "tosa.reshape"(%arg2) {new_shape = array<i64: 1, 1, 32, 8>}
+  // CHECK: %[[VAL_2:.*]] = "tosa.select"(%[[VAL_0]], %arg1, %[[VAL_1]])
+  %0 = "tosa.select"(%arg0, %arg1, %arg2) : (tensor<i1>, tensor<1x32x32x8xf32>, tensor<32x8xf32>) -> tensor<1x32x32x8xf32>
+  return %0 : tensor<1x32x32x8xf32>
+}
+
+// -----
+// CHECK-LABEL: broadcast_select_bac
+func.func @test_broadcast_select_bac(%arg0: tensor<32x8xi1>, %arg1: tensor<f32>, %arg2: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.reshape"(%arg0) {new_shape = array<i64: 1, 1, 32, 8>}
+  // CHECK-DAG: %[[VAL_1:.*]] = "tosa.reshape"(%arg1) {new_shape = array<i64: 1, 1, 1, 1>}
+  // CHECK: %[[VAL_2:.*]] = "tosa.select"(%[[VAL_0]], %[[VAL_1]], %arg2)
+  %0 = "tosa.select"(%arg0, %arg1, %arg2) : (tensor<32x8xi1>, tensor<f32>, tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32>
+  return %0 : tensor<1x32x32x8xf32>
+}
+
+// -----
+// CHECK-LABEL: broadcast_select_bca
+func.func @test_broadcast_select_bca(%arg0: tensor<32x8xi1>, %arg1: tensor<1x32x32x8xf32>, %arg2: tensor<i1>) -> tensor<1x32x32x8xf32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.reshape"(%arg0) {new_shape = array<i64: 1, 1, 32, 8>}
+  // CHECK-DAG: %[[VAL_1:.*]] = "tosa.reshape"(%arg2) {new_shape = array<i64: 1, 1, 1, 1>}
+  // CHECK: %[[VAL_2:.*]] = "tosa.select"(%[[VAL_0]], %arg1, %[[VAL_1]])
+  %0 = "tosa.select"(%arg0, %arg1, %arg2) : (tensor<32x8xi1>, tensor<1x32x32x8xf32>, tensor<i1>) -> tensor<1x32x32x8xf32>
+  return %0 : tensor<1x32x32x8xf32>
+}
+
+// -----
+// CHECK-LABEL: broadcast_select_cab
+func.func @test_broadcast_select_cab(%arg0: tensor<1x32x32x8xi1>, %arg1: tensor<f32>, %arg2: tensor<32x8xf32>) -> tensor<1x32x32x8xf32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.reshape"(%arg1) {new_shape = array<i64: 1, 1, 1, 1>}
+  // CHECK-DAG: %[[VAL_1:.*]] = "tosa.reshape"(%arg2) {new_shape = array<i64: 1, 1, 32, 8>}
+  // CHECK: %[[VAL_2:.*]] = "tosa.select"(%arg0, %[[VAL_0]], %[[VAL_1]])
+  %0 = "tosa.select"(%arg0, %arg1, %arg2) : (tensor<1x32x32x8xi1>, tensor<f32>, tensor<32x8xf32>) -> tensor<1x32x32x8xf32>
+  return %0 : tensor<1x32x32x8xf32>
+}
+
+// -----
+// CHECK-LABEL: broadcast_select_cba
+func.func @test_broadcast_select_cba(%arg0: tensor<1x32x32x8xi1>, %arg1: tensor<32x8xf32>, %arg2: tensor<i1>) -> tensor<1x32x32x8xf32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.reshape"(%arg1) {new_shape = array<i64: 1, 1, 32, 8>}
+  // CHECK-DAG: %[[VAL_1:.*]] = "tosa.reshape"(%arg2) {new_shape = array<i64: 1, 1, 1, 1>}
+  // CHECK: %[[VAL_2:.*]] = "tosa.select"(%arg0, %[[VAL_0]], %[[VAL_1]])
+  %0 = "tosa.select"(%arg0, %arg1, %arg2) : (tensor<1x32x32x8xi1>, tensor<32x8xf32>, tensor<i1>) -> tensor<1x32x32x8xf32>
+  return %0 : tensor<1x32x32x8xf32>
+}