diff --git a/mlir/lib/Conversion/TosaToLinalg/CMakeLists.txt b/mlir/lib/Conversion/TosaToLinalg/CMakeLists.txt
--- a/mlir/lib/Conversion/TosaToLinalg/CMakeLists.txt
+++ b/mlir/lib/Conversion/TosaToLinalg/CMakeLists.txt
@@ -15,6 +15,7 @@
   MLIRLinalgUtils
   MLIRMath
   MLIRPass
+  MLIRTensor
   MLIRTosa
   MLIRTosaTransforms
   MLIRSupport
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
@@ -14,6 +14,7 @@
 #include "mlir/Dialect/Linalg/IR/LinalgOps.h"
 #include "mlir/Dialect/Math/IR/Math.h"
 #include "mlir/Dialect/StandardOps/IR/Ops.h"
+#include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Dialect/Tosa/IR/TosaOps.h"
 #include "mlir/IR/Matchers.h"
 #include "mlir/IR/PatternMatch.h"
@@ -657,6 +658,83 @@
   }
 };
 
+class PadConverter : public OpRewritePattern<tosa::PadOp> {
+public:
+  using OpRewritePattern<tosa::PadOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tosa::PadOp padOp,
+                                PatternRewriter &rewriter) const final {
+    auto loc = padOp.getLoc();
+    auto input = padOp.input1();
+    auto padding = padOp.padding();
+
+    ShapedType inputTy = input.getType().cast<ShapedType>();
+    ShapedType paddingTy = padding.getType().cast<ShapedType>();
+    Type elementTy = inputTy.getElementType();
+
+    int64_t rank = inputTy.getRank();
+    if (rank != paddingTy.getDimSize(0)) {
+      return rewriter.notifyMatchFailure(
+          padOp, "Input rank does not match padding dim-0.");
+    }
+
+    if (!inputTy.hasStaticShape() || !paddingTy.hasStaticShape()) {
+      return rewriter.notifyMatchFailure(
+          padOp,
+          "Pad converter requires static shaped input / padding values.");
+    }
+
+    Value lowIndex = rewriter.create<ConstantOp>(loc, rewriter.getIndexAttr(0));
+    Value highIndex =
+        rewriter.create<ConstantOp>(loc, rewriter.getIndexAttr(1));
+
+    SmallVector<OpFoldResult, 3> lowValues;
+    SmallVector<OpFoldResult, 3> highValues;
+
+    lowValues.reserve(rank);
+    highValues.reserve(rank);
+
+    for (int i = 0; i < rank; i++) {
+      Value inputIndex = rewriter.createOrFold<ConstantIndexOp>(loc, i);
+      Value lowVal = rewriter.createOrFold<tensor::ExtractOp>(
+          loc, padding, ValueRange({inputIndex, lowIndex}));
+      Value highVal = rewriter.createOrFold<tensor::ExtractOp>(
+          loc, padding, ValueRange({inputIndex, highIndex}));
+
+      lowVal = rewriter.createOrFold<IndexCastOp>(loc, rewriter.getIndexType(),
+                                                  lowVal);
+      highVal = rewriter.createOrFold<IndexCastOp>(loc, rewriter.getIndexType(),
+                                                   highVal);
+
+      lowValues.push_back(lowVal);
+      highValues.push_back(highVal);
+    }
+
+    Value constant;
+    if (elementTy.isa<FloatType>())
+      constant = rewriter.create<ConstantOp>(
+          loc, rewriter.getFloatAttr(elementTy, 0.0));
+
+    if (elementTy.isa<IntegerType>() && padOp.quantization_info())
+      constant = rewriter.create<ConstantIntOp>(loc,
+                                                padOp.quantization_info()
+                                                    .getValue()
+                                                    .input_zp()
+                                                    .getValue()
+                                                    .getZExtValue(),
+                                                elementTy);
+
+    if (elementTy.isa<IntegerType>() && !padOp.quantization_info())
+      constant = rewriter.create<ConstantIntOp>(loc, 0, elementTy);
+
+    auto newPadOp = linalg::PadTensorOp::createPadScalarOp(
+        padOp.getType(), input, constant, lowValues, highValues, loc, rewriter);
+
+    rewriter.replaceOp(padOp, newPadOp.getResult());
+    return success();
+  }
+};
+
 } // namespace
 
 void mlir::tosa::populateTosaToLinalgOnTensorsConversionPatterns(
@@ -680,6 +758,6 @@
       IdentityNConverter<tosa::IdentityOp>,
       IdentityNConverter<tosa::IdentityNOp>, ReduceConverter<tosa::ReduceMinOp>,
       ReduceConverter<tosa::ReduceMaxOp>, ReduceConverter<tosa::ReduceSumOp>,
-      ReduceConverter<tosa::ReduceProdOp>, ReshapeOpConverter,
+      ReduceConverter<tosa::ReduceProdOp>, PadConverter, ReshapeOpConverter,
       TransposeConverter>(context);
 }
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgPass.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgPass.cpp
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgPass.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgPass.cpp
@@ -15,6 +15,7 @@
 #include "mlir/Dialect/Linalg/IR/LinalgOps.h"
 #include "mlir/Dialect/Math/IR/Math.h"
 #include "mlir/Dialect/StandardOps/IR/Ops.h"
+#include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Dialect/Tosa/IR/TosaOps.h"
 #include "mlir/Dialect/Tosa/Transforms/PassDetail.h"
 #include "mlir/Dialect/Tosa/Transforms/Passes.h"
@@ -31,14 +32,15 @@
     : public TosaToLinalgOnTensorsBase<TosaToLinalgOnTensors> {
 public:
   void getDependentDialects(DialectRegistry &registry) const override {
-    registry
-        .insert<linalg::LinalgDialect, math::MathDialect, StandardOpsDialect>();
+    registry.insert<linalg::LinalgDialect, math::MathDialect,
+                    StandardOpsDialect, tensor::TensorDialect>();
   }
 
   void runOnFunction() override {
     OwningRewritePatternList patterns;
     ConversionTarget target(getContext());
-    target.addLegalDialect<linalg::LinalgDialect, StandardOpsDialect>();
+    target.addLegalDialect<linalg::LinalgDialect, tensor::TensorDialect,
+                           StandardOpsDialect>();
     target.addIllegalDialect<tosa::TosaDialect>();
     target.markUnknownOpDynamicallyLegal([](Operation *) { return true; });
 
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
@@ -433,3 +433,46 @@
   %4 = "tosa.reduce_max"(%arg0) {axis = 0 : i64} : (tensor<5x4xi32>) -> tensor<4xi32>
   return
 }
+
+// -----
+
+func @pad_float(%arg0 : tensor<1x2xf32>) -> (tensor<4x9xf32>) {
+  %0 = constant dense<[[1, 2], [3, 4]]> : tensor<2x2xi32>
+  // CHECK: [[INDEX0:%.+]] = constant 0 : index
+  // CHECK: [[INDEX1:%.+]] = constant 1 : index
+  // CHECK: [[ROW0:%.+]] = constant 0 : index
+  // CHECK: [[LOW0:%.+]] = tensor.extract %cst{{\[}}[[ROW0]], [[INDEX0]]]
+  // CHECK: [[HIGH0:%.+]] = tensor.extract %cst{{\[}}[[ROW0]], [[INDEX1]]]
+  // CHECK: [[LOW0_IDX:%.+]] = index_cast %0
+  // CHECK: [[HIGH0_IDX:%.+]] = index_cast %1
+  // CHECK: [[ROW1:%.+]] = constant 1 : index
+  // CHECK: [[LOW1:%.+]] = tensor.extract %cst{{\[}}%c1_1, %c0]
+  // CHECK: [[HIGH1:%.+]] = tensor.extract %cst{{\[}}%c1_1, %c1]
+  // CHECK: [[LOW1_IDX:%.+]] = index_cast [[LOW1]]
+  // CHECK: [[HIGH1_IDX:%.+]] = index_cast [[HIGH1]]
+  // CHECK: [[CST:%.+]] = constant 0.000000e+00 : f32
+  // CHECK: %8 = linalg.pad_tensor %arg0 low{{\[}}[[LOW0_IDX]], [[LOW1_IDX]]] high{{\[}}[[HIGH0_IDX]], [[HIGH1_IDX]]]  {
+  // CHECK: ^bb0(%arg1: index, %arg2: index):  // no predecessors
+  // CHECK:   linalg.yield [[CST]]
+  // CHECK: } : tensor<1x2xf32> to tensor<4x9xf32>
+  %1 = "tosa.pad"(%arg0, %0)  : (tensor<1x2xf32>, tensor<2x2xi32>)  -> (tensor<4x9xf32>)
+  return %1 : tensor<4x9xf32>
+}
+
+func @pad_int(%arg0 : tensor<1x2xi32>) -> (tensor<4x9xi32>) {
+  %0 = constant dense<[[1, 2], [3, 4]]> : tensor<2x2xi32>
+  // CHECK: [[CST:%.+]] = constant 0 : i32
+  // CHECK: linalg.pad_tensor
+  // CHECK:   linalg.yield [[CST]]
+  %1 = "tosa.pad"(%arg0, %0)  : (tensor<1x2xi32>, tensor<2x2xi32>)  -> (tensor<4x9xi32>)
+  return %1 : tensor<4x9xi32>
+}
+
+func @pad_quant(%arg0 : tensor<1x2xi32>) -> (tensor<4x9xi32>) {
+  %0 = constant dense<[[1, 2], [3, 4]]> : tensor<2x2xi32>
+  // CHECK: [[CST:%.+]] = constant 42 : i32
+  // CHECK: linalg.pad_tensor
+  // CHECK:   linalg.yield [[CST]]
+  %1 = "tosa.pad"(%arg0, %0) { quantization_info = { input_zp = 42 : i32}} : (tensor<1x2xi32>, tensor<2x2xi32>)  -> (tensor<4x9xi32>)
+  return %1 : tensor<4x9xi32>
+}