diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td
@@ -174,10 +174,10 @@
 def Tosa_AvgPool2dOpQuantInfoBuilder : OpBuilder<
   (ins "::mlir::Type":$outputType, "::mlir::Value":$input,
        "::mlir::DenseI64ArrayAttr":$kernel, "::mlir::DenseI64ArrayAttr":$stride,
-       "::mlir::DenseI64ArrayAttr":$pad),
+       "::mlir::DenseI64ArrayAttr":$pad, "::mlir::TypeAttr":$acc_type),
   [{
     buildAvgPool2dOpWithQuantInfo($_builder, $_state, outputType,
-                                  input, kernel, stride, pad);
+                                  input, kernel, stride, pad, acc_type);
   }]>;
 
 // This builder is called on single-parameter unary operators that have a scale
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
@@ -53,6 +53,12 @@
   );
 }
 
+//===----------------------------------------------------------------------===//
+// Accumulator types.
+//===----------------------------------------------------------------------===//
+
+def Tosa_AccType : AnyTypeOf<[I<32>, SI<32>, F16, F32]>;
+
 //===----------------------------------------------------------------------===//
 // Operator: avg_pool2d
 //===----------------------------------------------------------------------===//
@@ -74,6 +80,7 @@
     Tosa_IntArrayAttr2:$kernel,
     Tosa_IntArrayAttr2:$stride,
     Tosa_IntArrayAttr4:$pad,
+    TypeAttrOf<Tosa_AccType>:$acc_type,
     OptionalAttr<Tosa_UnaryOpQuantizationAttr>:$quantization_info
   );
 
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -751,8 +751,7 @@
     ShapedType resultTy = cast<ShapedType>(op.getType());
     Type resultETy = cast<ShapedType>(op.getType()).getElementType();
 
-    Type accETy =
-        isa<IntegerType>(inElementTy) ? rewriter.getI32Type() : inElementTy;
+    Type accETy = op.getAccType();
     ShapedType accTy = resultTy.clone(accETy);
 
     auto dynamicDimsOr =
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -153,6 +153,17 @@
           llvm::dyn_cast<mlir::quant::UniformQuantizedType>(resultETy))
     resultETy = quantType.getStorageType();
 
+  auto accType = getAccType();
+  if (inputETy.isa<IntegerType>() && !accType.isInteger(32))
+    return emitOpError("accumulator type for integer tensor is not i32");
+
+  if ((inputETy.isBF16() || inputETy.isF16()) &&
+      !(accType.isF16() || accType.isF32()))
+    return emitOpError("accumulator type for f16/bf16 tensor is not f16/f32");
+
+  if (inputETy.isF32() && !accType.isF32())
+    return emitOpError("accumulator type for f32 tensor is not f32");
+
   if (inputETy.isF32() && resultETy.isF32())
     return success();
   if (inputETy.isInteger(8) && resultETy.isInteger(8))
@@ -268,13 +279,16 @@
 /// Both the tosa.avg_pool2d and unary ops use the same UnaruOpQuantizationAttr
 /// but avg_pool operator has its own builder as it has additional parameters
 /// not part of the unary ops.
-static void buildAvgPool2dOpWithQuantInfo(
-    OpBuilder &builder, OperationState &result, Type outputType, Value input,
-    DenseArrayAttr kernel, DenseArrayAttr stride, DenseArrayAttr pad) {
+static void
+buildAvgPool2dOpWithQuantInfo(OpBuilder &builder, OperationState &result,
+                              Type outputType, Value input,
+                              DenseArrayAttr kernel, DenseArrayAttr stride,
+                              DenseArrayAttr pad, TypeAttr acc_type) {
   result.addOperands(input);
   result.addAttribute("kernel", kernel);
   result.addAttribute("stride", stride);
   result.addAttribute("pad", pad);
+  result.addAttribute("acc_type", acc_type);
   auto quantAttr = buildUnaryOpQuantizationAttr(builder, input, outputType);
   if (quantAttr)
     result.addAttribute("quantization_info", quantAttr);
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
@@ -286,7 +286,7 @@
   // CHECK:   %[[FLT:.+]] = arith.sitofp %[[CAST]]
   // CHECK:   %[[DIV:.+]] = arith.divf %[[IN]], %[[FLT]]
   // CHECK:   linalg.yield %[[DIV]]
-  %0 = "tosa.avg_pool2d"(%arg0) {pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xf32>)  -> (tensor<1x5x33x62xf32>)
+  %0 = "tosa.avg_pool2d"(%arg0) {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xf32>)  -> (tensor<1x5x33x62xf32>)
   return %0 : tensor<1x5x33x62xf32>
 }
 
@@ -329,7 +329,7 @@
   // CHECK: %[[CLAMP:.+]] = arith.select %[[CMP]], %[[CMAX]], %[[SEL]]
   // CHECK: %[[TRUNC:.+]] = arith.trunci %[[CLAMP]]
   // CHECK: linalg.yield %[[TRUNC]]
-  %0 = "tosa.avg_pool2d"(%arg0) {pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xi8>)  -> (tensor<1x5x33x62xi8>)
+  %0 = "tosa.avg_pool2d"(%arg0) {acc_type = i32, pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xi8>)  -> (tensor<1x5x33x62xi8>)
   return %0 : tensor<1x5x33x62xi8>
 }
 
@@ -352,7 +352,7 @@
   // CHECK-SAME: outs(%[[FILL]] : tensor<?x5x33x62xf32>) -> tensor<?x5x33x62xf32>
   // CHECK: %[[EMPTY:.+]] = tensor.empty(%[[BATCH]]) : tensor<?x5x33x62xf32>
   // CHECK: %[[GENERIC:.+]] = linalg.generic
-  %0 = "tosa.avg_pool2d"(%arg0) {pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<?x6x34x62xf32>)  -> (tensor<?x5x33x62xf32>)
+  %0 = "tosa.avg_pool2d"(%arg0) {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<?x6x34x62xf32>)  -> (tensor<?x5x33x62xf32>)
   return %0 : tensor<?x5x33x62xf32>
 }
 
diff --git a/mlir/test/Dialect/Tosa/ops.mlir b/mlir/test/Dialect/Tosa/ops.mlir
--- a/mlir/test/Dialect/Tosa/ops.mlir
+++ b/mlir/test/Dialect/Tosa/ops.mlir
@@ -12,28 +12,28 @@
 // -----
 // CHECK-LABEL: avg_pool2d_f32
 func.func @test_avg_pool2d_f32(%arg0: tensor<1x7x7x9xf32>) -> tensor<1x7x7x9xf32> {
-    %0 = "tosa.avg_pool2d"(%arg0) {kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xf32>) -> tensor<1x7x7x9xf32>
+    %0 = "tosa.avg_pool2d"(%arg0) {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xf32>) -> tensor<1x7x7x9xf32>
     return %0 : tensor<1x7x7x9xf32>
 }
 
 // -----
 // CHECK-LABEL: avg_pool2d_i8
 func.func @test_avg_pool2d_i8(%arg0: tensor<1x7x7x9xi8>) -> tensor<1x7x7x9xi8> {
-    %0 = "tosa.avg_pool2d"(%arg0) {kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xi8>) -> tensor<1x7x7x9xi8>
+    %0 = "tosa.avg_pool2d"(%arg0) {acc_type = i32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xi8>) -> tensor<1x7x7x9xi8>
     return %0 : tensor<1x7x7x9xi8>
 }
 
 // -----
 // CHECK-LABEL: avg_pool2d_i16
 func.func @test_avg_pool2d_i16(%arg0: tensor<1x7x7x9xi16>) -> tensor<1x7x7x9xi16> {
-    %0 = "tosa.avg_pool2d"(%arg0) {kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xi16>) -> tensor<1x7x7x9xi16>
+    %0 = "tosa.avg_pool2d"(%arg0) {acc_type = i32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9xi16>) -> tensor<1x7x7x9xi16>
     return %0 : tensor<1x7x7x9xi16>
 }
 
 // -----
 // CHECK-LABEL: avg_pool2d_q8
 func.func @test_avg_pool2d_q8(%arg0: tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>> {
-    %0 = "tosa.avg_pool2d"(%arg0) {kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
+    %0 = "tosa.avg_pool2d"(%arg0) {acc_type = i32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
     return %0 : tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
 }
 
diff --git a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
--- a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
@@ -659,7 +659,7 @@
 // CHECK-LABEL: @test_pool_static
 func.func @test_pool_static(%arg0: tensor<3x5x6x7xf32>) {
   // CHECK: -> tensor<3x2x4x7xf32>
-  %0 = "tosa.avg_pool2d"(%arg0) {kernel = array<i64: 4, 3>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<3x5x6x7xf32>) -> tensor<?x?x?x?xf32>
+  %0 = "tosa.avg_pool2d"(%arg0) {acc_type = f32, kernel = array<i64: 4, 3>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<3x5x6x7xf32>) -> tensor<?x?x?x?xf32>
 
   // CHECK: -> tensor<3x2x4x7xf32>
   %1 = "tosa.max_pool2d"(%arg0) {kernel = array<i64: 4, 3>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<3x5x6x7xf32>) -> tensor<?x?x?x?xf32>
@@ -689,7 +689,7 @@
 // CHECK-LABEL: @test_pool_dynamic_input
 func.func @test_pool_dynamic_input(%arg0: tensor<?x?x?x?xf32>) {
   // CHECK: -> tensor<?x?x?x?xf32>
-  %0 = "tosa.avg_pool2d"(%arg0) {kernel = array<i64: 4, 3>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+  %0 = "tosa.avg_pool2d"(%arg0) {acc_type = f32, kernel = array<i64: 4, 3>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
 
   // CHECK: -> tensor<?x?x?x?xf32>
   %1 = "tosa.max_pool2d"(%arg0) {kernel = array<i64: 4, 3>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
@@ -701,7 +701,7 @@
 // CHECK-LABEL: @test_pool_padded
 func.func @test_pool_padded(%arg0: tensor<3x5x6x7xf32>) {
   // CHECK: -> tensor<3x5x11x7xf32>
-  %0 = "tosa.avg_pool2d"(%arg0) {kernel = array<i64: 4, 3>, pad = array<i64: 1, 2, 3, 4>, stride = array<i64: 1, 1>} : (tensor<3x5x6x7xf32>) -> tensor<?x?x?x?xf32>
+  %0 = "tosa.avg_pool2d"(%arg0) {acc_type = f32, kernel = array<i64: 4, 3>, pad = array<i64: 1, 2, 3, 4>, stride = array<i64: 1, 1>} : (tensor<3x5x6x7xf32>) -> tensor<?x?x?x?xf32>
 
   // CHECK: -> tensor<3x5x11x7xf32>
   %1 = "tosa.max_pool2d"(%arg0) {kernel = array<i64: 4, 3>, pad = array<i64: 1, 2, 3, 4>, stride = array<i64: 1, 1>} : (tensor<3x5x6x7xf32>) -> tensor<?x?x?x?xf32>
@@ -731,7 +731,7 @@
 // CHECK-LABEL: @test_pool_stride
 func.func @test_pool_stride(%arg0: tensor<3x11x12x7xf32>) {
   // CHECK: -> tensor<3x4x4x7xf32>
-  %0 = "tosa.avg_pool2d"(%arg0) {kernel = array<i64: 4, 3>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 3>} : (tensor<3x11x12x7xf32>) -> tensor<?x?x?x?xf32>
+  %0 = "tosa.avg_pool2d"(%arg0) {acc_type = f32, kernel = array<i64: 4, 3>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 3>} : (tensor<3x11x12x7xf32>) -> tensor<?x?x?x?xf32>
 
   // CHECK: -> tensor<3x4x4x7xf32>
   %1 = "tosa.max_pool2d"(%arg0) {kernel = array<i64: 4, 3>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 3>} : (tensor<3x11x12x7xf32>) -> tensor<?x?x?x?xf32>