diff --git a/include/ttmlir/Dialect/TTIR/IR/TTIROps.td b/include/ttmlir/Dialect/TTIR/IR/TTIROps.td
index c0e5a78b84..6e7eeeb55f 100644
--- a/include/ttmlir/Dialect/TTIR/IR/TTIROps.td
+++ b/include/ttmlir/Dialect/TTIR/IR/TTIROps.td
@@ -326,6 +326,47 @@ def TTIR_ReluOp : TTIR_ElementwiseUnaryOp<"relu"> {
     }];
 }
 
+def TTIR_RoundOp : TTIR_DPSOp<"round"> {
+    let summary = "Eltwise round.";
+    let description = [{
+      Eltwise round operation.
+    }];
+    let arguments = (ins
+        AnyRankedTensor:$input,
+        AnyRankedTensor:$output,
+        DefaultValuedAttr<I32Attr, "1">:$decimals
+    );
+
+    let extraClassDeclaration = [{
+      MutableOperandRange getDpsInitsMutable() { return getOutputMutable(); }
+    }];
+
+    let results = (outs AnyRankedTensor:$result);
+}
+
+def TTIR_RoundNearestEvenOp : TTIR_DPSOp<"roundnearesteven"> {
+    let summary = "Eltwise round towards nearest even.";
+    let description = [{
+      Rounds a number to the nearest value. If the number is exactly halfway between two values, the value is rounded to the nearest even value.
+
+      Example:
+      // %operand = [-2.5, 0.4, 0.5, 0.6, 2.5]
+      %result = "stablehlo.round_nearest_even"(%operand) : (tensor<5xf64>) -> tensor<5xf64>
+      // %result: [-2.0, 0.0, 0.0, 1.0, 2.0]
+    }];
+    let arguments = (ins
+        AnyRankedTensor:$input,
+        AnyRankedTensor:$output,
+        DefaultValuedAttr<I32Attr, "0">:$decimals
+    );
+
+    let extraClassDeclaration = [{
+      MutableOperandRange getDpsInitsMutable() { return getOutputMutable(); }
+    }];
+
+    let results = (outs AnyRankedTensor:$result);
+}
+
 def TTIR_RsqrtOp : TTIR_ElementwiseUnaryOp<"rsqrt"> {
     let summary = "Eltwise reciprocal square root.";
     let description = [{
diff --git a/include/ttmlir/Dialect/TTNN/IR/TTNNOps.td b/include/ttmlir/Dialect/TTNN/IR/TTNNOps.td
index 7567364d10..87f6ae5448 100644
--- a/include/ttmlir/Dialect/TTNN/IR/TTNNOps.td
+++ b/include/ttmlir/Dialect/TTNN/IR/TTNNOps.td
@@ -296,6 +296,20 @@ def TTNN_ReluOp : TTNN_ElementwiseUnaryOp<"relu",
     }];
 }
 
+def TTNN_RoundOp : TTNN_Op<"round"> {
+    let summary = "Eltwise round operation.";
+    let description = [{
+      Eltwise round with decimal option to choose between Banker's rounding or normal rounding.
+    }];
+
+    let arguments = (ins
+        Variadic<AnyRankedTensor>:$inputs,
+        I32Attr:$decimals
+    );
+
+    let results = (outs Variadic<AnyRankedTensor>:$result);
+}
+
 def TTNN_SinOp : TTNN_ElementwiseUnaryOp<"sin"> {
     let summary = "Eltwise sine.";
     let description = [{
diff --git a/include/ttmlir/Target/TTNN/program.fbs b/include/ttmlir/Target/TTNN/program.fbs
index 4ba5443ad5..2495be1388 100644
--- a/include/ttmlir/Target/TTNN/program.fbs
+++ b/include/ttmlir/Target/TTNN/program.fbs
@@ -140,7 +140,8 @@ enum EltwiseOpType: uint32 {
   LeakyRelu,
   Scatter,
   Tan,
-  Tanh
+  Tanh,
+  Round
 }
 
 table ClampOpParams {
@@ -148,6 +149,10 @@ table ClampOpParams {
   max: float;
 }
 
+table RoundOpParams {
+  decimals: int32;
+}
+
 table EltwiseOpWithFloatParams {
   parameter: float;
 }
@@ -155,6 +160,7 @@ table EltwiseOpWithFloatParams {
 union EltwiseOpParams {
   ClampOpParams,
   EltwiseOpWithFloatParams,
+  RoundOpParams,
 }
 
 table EltwiseOp {
diff --git a/lib/Conversion/StableHLOToTTIR/StableHLOToTTIRPatterns.cpp b/lib/Conversion/StableHLOToTTIR/StableHLOToTTIRPatterns.cpp
index b541d0a3e3..ab580293f1 100644
--- a/lib/Conversion/StableHLOToTTIR/StableHLOToTTIRPatterns.cpp
+++ b/lib/Conversion/StableHLOToTTIR/StableHLOToTTIRPatterns.cpp
@@ -1755,6 +1755,41 @@ class StableHLOToTTIROpReverseOpConversionPattern
   }
 };
 
+template <typename SrcOp, typename DestOp,
+          typename Adaptor = typename SrcOp::Adaptor>
+class StableHLOToTTIRRoundOpConversionPattern
+    : public OpConversionPattern<SrcOp> {
+public:
+  using OpConversionPattern<SrcOp>::OpConversionPattern;
+
+  LogicalResult
+  matchAndRewrite(SrcOp srcOp, Adaptor adaptor,
+                  ConversionPatternRewriter &rewriter) const override {
+
+    auto outputType = mlir::cast<RankedTensorType>(
+        this->getTypeConverter()->convertType(srcOp.getResult().getType()));
+
+    tensor::EmptyOp outputTensor = rewriter.create<tensor::EmptyOp>(
+        srcOp.getLoc(), outputType.getShape(), outputType.getElementType());
+
+    if (isa<stablehlo::RoundOp>(srcOp)) {
+      rewriter.replaceOpWithNewOp<DestOp>(srcOp, outputType,
+                                          adaptor.getOperand(), outputTensor,
+                                          rewriter.getI32IntegerAttr(1));
+    } else if (isa<stablehlo::RoundNearestEvenOp>(srcOp)) {
+      rewriter.replaceOpWithNewOp<DestOp>(srcOp, outputType,
+                                          adaptor.getOperand(), outputTensor,
+                                          rewriter.getI32IntegerAttr(0));
+    } else {
+      return rewriter.notifyMatchFailure(
+          srcOp, "ttir::RoundOp only supports stablehlo:RoundOp or "
+                 "stablehlo::RoundNearestEvenOp");
+    }
+
+    return success();
+  }
+};
+
 void addElementwiseUnaryOpsConversionPatterns(MLIRContext *ctx,
                                               RewritePatternSet &patterns,
                                               TypeConverter &typeConverter) {
@@ -1963,6 +1998,15 @@ void addReverseOpConversionPattern(MLIRContext *ctx,
   patterns.add<StableHLOToTTIROpReverseOpConversionPattern>(typeConverter, ctx);
 }
 
+void addRoundOpConversionPattern(MLIRContext *ctx, RewritePatternSet &patterns,
+                                 TypeConverter &typeConverter) {
+  patterns.add<StableHLOToTTIRRoundOpConversionPattern<
+      mlir::stablehlo::RoundOp, mlir::tt::ttir::RoundOp>>(typeConverter, ctx);
+  patterns.add<StableHLOToTTIRRoundOpConversionPattern<
+      mlir::stablehlo::RoundNearestEvenOp, mlir::tt::ttir::RoundNearestEvenOp>>(
+      typeConverter, ctx);
+}
+
 } // namespace
 
 namespace mlir::tt {
@@ -1992,6 +2036,7 @@ void populateStableHLOToTTIRPatterns(MLIRContext *ctx,
   addScatterOpConversionPatterns(ctx, patterns, typeConverter);
   addReturnOpConversionPatterns(ctx, patterns, typeConverter);
   addReverseOpConversionPattern(ctx, patterns, typeConverter);
+  addRoundOpConversionPattern(ctx, patterns, typeConverter);
 }
 
 } // namespace mlir::tt
diff --git a/lib/Conversion/TTIRToTTNN/TTIRToTTNN.cpp b/lib/Conversion/TTIRToTTNN/TTIRToTTNN.cpp
index a2b63a1bce..91261e35b6 100644
--- a/lib/Conversion/TTIRToTTNN/TTIRToTTNN.cpp
+++ b/lib/Conversion/TTIRToTTNN/TTIRToTTNN.cpp
@@ -965,6 +965,77 @@ class TypecastOpConversionPattern
   }
 };
 
+class BroadcastOpConversionPattern
+    : public OpConversionPattern<ttir::BroadcastOp> {
+  using OpConversionPattern<ttir::BroadcastOp>::OpConversionPattern;
+
+public:
+  LogicalResult
+  matchAndRewrite(ttir::BroadcastOp srcOp, ttir::BroadcastOp::Adaptor adaptor,
+                  ConversionPatternRewriter &rewriter) const override {
+
+    // Fold this operation into all consumer ops. It will only work with TTNN
+    // ops that support implicit broadcasting. We expect each Op's verify
+    // function to assert their arguments to verify that they can broadcast.
+
+    if (srcOp->getUsers().empty()) {
+      // This broadcast chain has already been replaced.
+      rewriter.eraseOp(srcOp);
+      return success();
+    }
+
+    mlir::Value input = srcOp.getOperand(0);
+
+    mlir::Operation *nextOp = srcOp;
+    while (isa<ttir::BroadcastOp>(*nextOp->getUsers().begin())) {
+      assert(nextOp->hasOneUse() &&
+             "Broadcast with multiple uses are not supported");
+      nextOp = *nextOp->getUsers().begin();
+      if (nextOp->getUsers().empty()) {
+        // This broadcast chain has already been replaced.
+        rewriter.eraseOp(srcOp);
+        return success();
+      }
+    }
+
+    rewriter.replaceAllOpUsesWith(nextOp, input);
+    rewriter.eraseOp(srcOp);
+
+    return success();
+  }
+};
+
+template <typename TTIROpTy, typename TTNNOpTy,
+          typename OpAdaptor = typename TTIROpTy::Adaptor>
+class RoundOpConversionPattern : public OpConversionPattern<TTIROpTy> {
+public:
+  using OpConversionPattern<TTIROpTy>::OpConversionPattern;
+
+  LogicalResult
+  matchAndRewrite(TTIROpTy op, OpAdaptor adaptor,
+                  ConversionPatternRewriter &rewriter) const override {
+
+    if (!isa<ttir::RoundOp>(op) && !isa<ttir::RoundNearestEvenOp>(op)) {
+      return rewriter.notifyMatchFailure(
+          op, "ttnn::RoundOp only supports ttir:RoundOp or "
+              "ttir::RoundNearestEvenOp");
+    }
+    if (isa<ttir::RoundOp>(op) && adaptor.getDecimals() == 0) {
+      return rewriter.notifyMatchFailure(
+          op, "ttir::RoundOp requires decimals != 0");
+    }
+    if (isa<ttir::RoundNearestEvenOp>(op) && adaptor.getDecimals() != 0) {
+      return rewriter.notifyMatchFailure(
+          op, "ttir::RoundNearestEvenOp requires decimals == 0");
+    }
+    rewriter.replaceOpWithNewOp<TTNNOpTy>(
+        op, this->getTypeConverter()->convertType(op.getType()),
+        adaptor.getInput(), adaptor.getDecimals());
+
+    return success();
+  }
+};
+
 class SubtractOpConversionPattern
     : public OpConversionPattern<ttir::SubtractOp> {
   using OpConversionPattern<ttir::SubtractOp>::OpConversionPattern;
@@ -1178,6 +1249,8 @@ void populateTTIRToTTNNPatterns(MLIRContext *ctx, RewritePatternSet &patterns,
            ReductionOpConversionPattern<ttir::MeanOp, ttnn::MeanOp>,
            ReductionOpConversionPattern<ttir::MaxOp, ttnn::MaxOp>,
            ElementwiseUnaryWithFloatParameterOpConversionPattern<ttir::LeakyReluOp, ttnn::LeakyReluOp>,
+           RoundOpConversionPattern<ttir::RoundOp, ttnn::RoundOp>,
+           RoundOpConversionPattern<ttir::RoundNearestEvenOp, ttnn::RoundOp>,
            EmbeddingOpConversionPattern,
            EmbeddingBackwardOpConversionPattern,
            SoftmaxOpConversionPattern,
diff --git a/lib/Conversion/TTNNToEmitC/TTNNToEmitC.cpp b/lib/Conversion/TTNNToEmitC/TTNNToEmitC.cpp
index 624ceddc33..d9e528cee1 100644
--- a/lib/Conversion/TTNNToEmitC/TTNNToEmitC.cpp
+++ b/lib/Conversion/TTNNToEmitC/TTNNToEmitC.cpp
@@ -678,6 +678,7 @@ void populateTTNNToEmitCPatterns(mlir::MLIRContext *ctx,
   patterns.add<DefaultOpConversionPattern<ttnn::AbsOp>,
                DefaultOpConversionPattern<ttnn::CbrtOp>,
                DefaultOpConversionPattern<ttnn::ClampOp>,
+               DefaultOpConversionPattern<ttnn::RoundOp>,
                DefaultOpConversionPattern<ttnn::FloorOp>,
                DefaultOpConversionPattern<ttnn::IsFiniteOp>,
                DefaultOpConversionPattern<ttnn::LogicalNotOp>,
@@ -692,6 +693,7 @@ void populateTTNNToEmitCPatterns(mlir::MLIRContext *ctx,
                DefaultOpConversionPattern<ttnn::SigmoidOp>,
                DefaultOpConversionPattern<ttnn::Log1pOp>,
                DefaultOpConversionPattern<ttnn::ReciprocalOp>,
+               DefaultOpConversionPattern<ttnn::RoundOp>,
                DefaultOpConversionPattern<ttnn::ExpOp>,
                DefaultOpConversionPattern<ttnn::CeilOp>,
                DefaultOpConversionPattern<ttnn::SinOp>,
diff --git a/lib/Target/TTNN/TTNNToFlatbuffer.cpp b/lib/Target/TTNN/TTNNToFlatbuffer.cpp
index b7ff1d7d57..03a160f519 100644
--- a/lib/Target/TTNN/TTNNToFlatbuffer.cpp
+++ b/lib/Target/TTNN/TTNNToFlatbuffer.cpp
@@ -530,6 +530,10 @@ createEltwiseOpParams(FlatbufferObjectCache &cache, EltwiseOp op) {
     return ::tt::target::ttnn::CreateEltwiseOpWithFloatParams(*cache.fbb,
                                                               parameter);
   }
+  if constexpr (std::is_same_v<EltwiseOp, RoundOp>) {
+    auto decimals = op.getDecimals();
+    return ::tt::target::ttnn::CreateRoundOpParams(*cache.fbb, decimals);
+  }
 }
 
 ::flatbuffers::Offset<::tt::target::ttnn::UpdateCacheOp>
@@ -569,6 +573,12 @@ createNonDPSEltwiseOp(FlatbufferObjectCache &cache, EltwiseOp op) {
     params = createEltwiseOpParams<ClampOp, ::tt::target::ttnn::ClampOpParams>(
                  cache, op)
                  .Union();
+  } else if (std::is_same_v<EltwiseOp, RoundOp>) {
+    type = ::tt::target::ttnn::EltwiseOpType::Round;
+    paramsType = ::tt::target::ttnn::EltwiseOpParams::RoundOpParams;
+    params = createEltwiseOpParams<RoundOp, ::tt::target::ttnn::RoundOpParams>(
+                 cache, op)
+                 .Union();
   } else {
     llvm_unreachable("unhandled non-DPS EltwiseOp");
   }
@@ -1098,6 +1108,10 @@ emitTTNNOperation(FlatbufferObjectCache &cache, Operation *op,
     return createOperation(cache, createNonDPSEltwiseOp(cache, clampOp),
                            debugString, locInfo);
   }
+  if (auto roundOp = dyn_cast<RoundOp>(op); roundOp) {
+    return createOperation(cache, createNonDPSEltwiseOp(cache, roundOp),
+                           debugString, locInfo);
+  }
   if (auto conv2dOp = dyn_cast<Conv2dOp>(op); conv2dOp) {
     return createOperation(cache, createOp(cache, conv2dOp), debugString,
                            locInfo);
diff --git a/runtime/lib/ttnn/operations/eltwise/unary/unary_composite.cpp b/runtime/lib/ttnn/operations/eltwise/unary/unary_composite.cpp
index 632377c2f4..0e13658568 100644
--- a/runtime/lib/ttnn/operations/eltwise/unary/unary_composite.cpp
+++ b/runtime/lib/ttnn/operations/eltwise/unary/unary_composite.cpp
@@ -43,6 +43,21 @@ static void runEltwiseUnaryCompositeClampOp(
   tensorPool.insert_or_assign(op->out()->global_id(), out);
 }
 
+static void runEltwiseUnaryCompositeRoundOp(
+    const ::tt::target::ttnn::EltwiseOp *op, ProgramTensorPool &tensorPool,
+    const std::function<::ttnn::Tensor(const ::ttnn::Tensor &, int,
+                                       const ::tt::tt_metal::MemoryConfig &)>
+        &ttnnOp) {
+  ::ttnn::Tensor *in = nullptr;
+  getEltwiseUnaryOpInputTensor(op, tensorPool, &in);
+
+  int32_t decimals = op->params_as_RoundOpParams()->decimals();
+  ::tt::tt_metal::MemoryConfig outputMemoryConfig =
+      ::tt::runtime::ttnn::utils::createMemoryConfig(op->out());
+  ::ttnn::Tensor out = ttnnOp(*in, decimals, outputMemoryConfig);
+  tensorPool.insert_or_assign(op->out()->global_id(), out);
+}
+
 void run(const ::tt::target::ttnn::EltwiseOp *op, ProgramContext &context) {
   ProgramTensorPool &tensorPool = context.getTensorPool();
   switch (op->type()) {
@@ -58,6 +73,10 @@ void run(const ::tt::target::ttnn::EltwiseOp *op, ProgramContext &context) {
     runEltwiseUnaryCompositeOp(op, tensorPool, ::ttnn::log1p);
     break;
   }
+  case ::tt::target::ttnn::EltwiseOpType::Round: {
+    runEltwiseUnaryCompositeRoundOp(op, tensorPool, ::ttnn::round);
+    break;
+  }
   default:
     LOG_FATAL("Unsupported Eltwise Binary Composite operation");
   }
diff --git a/runtime/lib/ttnn/operations/eltwise/unary/unary_composite.h b/runtime/lib/ttnn/operations/eltwise/unary/unary_composite.h
index f0c0c834c9..bcc441d142 100644
--- a/runtime/lib/ttnn/operations/eltwise/unary/unary_composite.h
+++ b/runtime/lib/ttnn/operations/eltwise/unary/unary_composite.h
@@ -15,6 +15,7 @@ inline bool isUnaryCompositeOp(const ::tt::target::ttnn::EltwiseOp *op) {
   case ::tt::target::ttnn::EltwiseOpType::Cbrt:
   case ::tt::target::ttnn::EltwiseOpType::Clamp:
   case ::tt::target::ttnn::EltwiseOpType::Log1p:
+  case ::tt::target::ttnn::EltwiseOpType::Round:
     return true;
   default:
     return false;
diff --git a/test/ttmlir/Conversion/StableHLOToTTIR/round_op.mlir b/test/ttmlir/Conversion/StableHLOToTTIR/round_op.mlir
new file mode 100644
index 0000000000..d49d206222
--- /dev/null
+++ b/test/ttmlir/Conversion/StableHLOToTTIR/round_op.mlir
@@ -0,0 +1,16 @@
+// REQUIRES: stablehlo
+// RUN: ttmlir-opt --stablehlo-to-ttir-pipeline %s | FileCheck %s
+module @jit_eltwise_round attributes {} {
+  func.func public @test_round(%arg0: tensor<4xbf16>) -> tensor<4xbf16> {
+    %0 = stablehlo.round_nearest_afz %arg0 : tensor<4xbf16>
+    // CHECK: %[[C:.*]] = tensor.empty[[C:.*]]
+    // CHECK: %[[C:.*]] = "ttir.round"[[C:.*]]
+    return %0 : tensor<4xbf16>
+  }
+  func.func public @test_roundnearesteven(%arg0: tensor<4xbf16>) -> tensor<4xbf16> {
+    %0 = stablehlo.round_nearest_even %arg0 : tensor<4xbf16>
+    // CHECK: %[[C:.*]] = tensor.empty[[C:.*]]
+    // CHECK: %[[C:.*]] = "ttir.roundnearesteven"[[C:.*]]
+    return %0 : tensor<4xbf16>
+  }
+}
diff --git a/test/ttmlir/Dialect/TTNN/simple_round.mlir b/test/ttmlir/Dialect/TTNN/simple_round.mlir
new file mode 100644
index 0000000000..6d3de5c7e8
--- /dev/null
+++ b/test/ttmlir/Dialect/TTNN/simple_round.mlir
@@ -0,0 +1,15 @@
+// RUN: ttmlir-opt --ttir-to-ttnn-backend-pipeline %s| FileCheck %s
+module attributes {} {
+  func.func @roundnearesteven(%arg0: tensor<4xbf16>) -> tensor<4xbf16> {
+    %0 = tensor.empty() : tensor<4xbf16>
+    // CHECK: %[[C:.*]] = "ttnn.round"[[C:.*]]
+    %1 = "ttir.roundnearesteven"(%arg0, %0) <{decimals = 0 : i32}> : (tensor<4xbf16>, tensor<4xbf16>) -> tensor<4xbf16>
+    return %1 : tensor<4xbf16>
+  }
+  func.func @round(%arg0: tensor<4xbf16>) -> tensor<4xbf16> {
+    %0 = tensor.empty() : tensor<4xbf16>
+    // CHECK: %[[C:.*]] = "ttnn.round"[[C:.*]]
+    %1 = "ttir.round"(%arg0, %0) <{decimals = 1 : i32}> : (tensor<4xbf16>, tensor<4xbf16>) -> tensor<4xbf16>
+    return %1 : tensor<4xbf16>
+  }
+}
diff --git a/test/ttmlir/Silicon/TTNN/simple_eltwise.mlir b/test/ttmlir/Silicon/TTNN/simple_eltwise.mlir
index a0452f01f8..202f8419f4 100644
--- a/test/ttmlir/Silicon/TTNN/simple_eltwise.mlir
+++ b/test/ttmlir/Silicon/TTNN/simple_eltwise.mlir
@@ -271,6 +271,20 @@ func.func @remainder(%arg0: tensor<32x32xf32>, %arg1: tensor<32x32xf32>) -> tens
   // CHECK: return {{.*}} : tensor<32x32xf32, {{.*}}
 }
 
+func.func @roundnearesteven(%arg0: tensor<4xbf16>) -> tensor<4xbf16> {
+  %0 = tensor.empty() : tensor<4xbf16>
+  // CHECK: %[[C:.*]] = "ttnn.round"[[C:.*]]
+  %1 = "ttir.roundnearesteven"(%arg0, %0) <{decimals = 0 : i32}> : (tensor<4xbf16>, tensor<4xbf16>) -> tensor<4xbf16>
+  return %1 : tensor<4xbf16>
+}
+
+func.func @round(%arg0: tensor<4xbf16>) -> tensor<4xbf16> {
+  %0 = tensor.empty() : tensor<4xbf16>
+  // CHECK: %[[C:.*]] = "ttnn.round"[[C:.*]]
+  %1 = "ttir.round"(%arg0, %0) <{decimals = 1 : i32}> : (tensor<4xbf16>, tensor<4xbf16>) -> tensor<4xbf16>
+  return %1 : tensor<4xbf16>
+}
+
 func.func @get_dimension_size(%arg0: tensor<13x21x3xf32>) -> tensor<1xi32> {
   %0 = "ttir.get_dimension_size"(%arg0) <{dimension = 1 : i32}> : (tensor<13x21x3xf32>) -> tensor<1xi32>
   // CHECK: [[VAL:%[0-9]+]] = "ttnn.full"(%{{[0-9]+}}) <{fillValue = 2.100000e+01 : f32}> : (!tt.device<#device>) -> tensor<1xi32, {{.*}}>