diff --git a/src/brevitas/core/function_wrapper/__init__.py b/src/brevitas/core/function_wrapper/__init__.py
index bf7c84e60..9d5d929d6 100644
--- a/src/brevitas/core/function_wrapper/__init__.py
+++ b/src/brevitas/core/function_wrapper/__init__.py
@@ -4,6 +4,7 @@
 from .clamp import ClampMin
 from .clamp import ScalarClamp
 from .clamp import TensorClamp
+from .learned_round import LearnedRoundSte
 from .misc import Identity
 from .misc import InplaceLogTwo
 from .misc import LogTwo
@@ -21,3 +22,4 @@
 from .shape import OverOutputChannelView
 from .shape import OverTensorView
 from .shape import StatsInputViewShapeImpl
+from .stochastic_round import StochasticRoundSte
diff --git a/src/brevitas/core/function_wrapper/stochastic_round.py b/src/brevitas/core/function_wrapper/stochastic_round.py
new file mode 100644
index 000000000..529038d7b
--- /dev/null
+++ b/src/brevitas/core/function_wrapper/stochastic_round.py
@@ -0,0 +1,46 @@
+# Copyright (C) 2023, Advanced Micro Devices, Inc. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+
+import torch
+
+from brevitas.function.ops_ste import round_ste
+
+
+def stochastic_round_ste_fn(generator):
+
+    class StochasticRoundSteFn(torch.autograd.Function):
+
+        @staticmethod
+        def forward(ctx, x):
+            floor_x = torch.floor(x)
+            x_diff = torch.abs(x - floor_x)
+            prob = torch.bernoulli(x_diff, generator=generator)
+            out = torch.where(prob.to(torch.bool), floor_x + 1., floor_x)
+            return out
+
+        @staticmethod
+        def backward(ctx, x_grad):
+            return x_grad
+
+    return StochasticRoundSteFn.apply
+
+
+class StochasticRoundSte(torch.nn.Module):
+
+    def __init__(self, deterministic_inference=True, seed=None, device=None) -> None:
+        super().__init__()
+        self.generator = torch.Generator(device=device)
+        if seed is not None:
+            self.generator.manual_seed(seed)
+        self.round_fn = stochastic_round_ste_fn(self.generator)
+        self.deterministic_inference = deterministic_inference
+        if deterministic_inference:
+            self.inference_fn = round_ste
+        else:
+            self.inference_fn = None
+
+    @torch.jit.ignore
+    def forward(self, x):
+        if self.deterministic_inference is not None and not self.training:
+            return self.inference_fn(x)
+        return self.round_fn(x)
diff --git a/src/brevitas/inject/enum.py b/src/brevitas/inject/enum.py
index 4b62d71b8..95ea05355 100644
--- a/src/brevitas/inject/enum.py
+++ b/src/brevitas/inject/enum.py
@@ -45,6 +45,7 @@ class FloatToIntImplType(AutoName):
     ROUND_TO_ZERO = auto()
     DPU = auto()
     LEARNED_ROUND = auto()
+    STOCHASTIC_ROUND = auto()
 
 
 class LearnedRoundImplType(AutoName):
diff --git a/src/brevitas/quant/experimental/float_base.py b/src/brevitas/quant/experimental/float_base.py
index 7c3738226..f1d191057 100644
--- a/src/brevitas/quant/experimental/float_base.py
+++ b/src/brevitas/quant/experimental/float_base.py
@@ -1,7 +1,6 @@
 # Copyright (C) 2023, Advanced Micro Devices, Inc. All rights reserved.
 # SPDX-License-Identifier: BSD-3-Clause
 
-from brevitas.core.function_wrapper import RoundSte
 from brevitas.core.quant.float import FloatQuant
 from brevitas.core.scaling.float_scaling import FloatScaling
 from brevitas.inject import ExtendedInjector
@@ -9,20 +8,21 @@
 from brevitas.proxy.runtime_quant import ActQuantProxyFromInjector
 from brevitas.quant.solver import ActQuantSolver
 from brevitas.quant.solver import WeightQuantSolver
+from brevitas.quant.solver.common import SolveTensorQuantFloatToIntImplFromEnum
 
 
-class FloatWeightBase(ExtendedInjector):
+class FloatWeightBase(SolveTensorQuantFloatToIntImplFromEnum):
     proxy_class = WeightQuantProxyFromInjector
     tensor_quant = FloatQuant
     signed = True
-    float_to_int_impl = RoundSte
+    float_to_int_impl_type = 'round'
 
 
-class FloatActBase(ExtendedInjector):
+class FloatActBase(SolveTensorQuantFloatToIntImplFromEnum):
     proxy_class = ActQuantProxyFromInjector
     tensor_quant = FloatQuant
     signed = True
-    float_to_int_impl = RoundSte
+    float_to_int_impl_type = 'round'
 
 
 class ScaledFloatWeightBase(FloatWeightBase, WeightQuantSolver):
diff --git a/src/brevitas/quant/solver/common.py b/src/brevitas/quant/solver/common.py
index 55b295253..2e5e1e982 100644
--- a/src/brevitas/quant/solver/common.py
+++ b/src/brevitas/quant/solver/common.py
@@ -6,6 +6,7 @@
 from brevitas.core.function_wrapper.learned_round import LearnedRoundHardSigmoid
 from brevitas.core.function_wrapper.learned_round import LearnedRoundSigmoid
 from brevitas.core.function_wrapper.learned_round import LearnedRoundSte
+from brevitas.core.function_wrapper.stochastic_round import StochasticRoundSte
 from brevitas.core.quant import *
 from brevitas.core.quant import QuantType
 from brevitas.core.restrict_val import *
@@ -45,6 +46,8 @@ def solve_float_to_int_impl_from_enum(impl_type):
         return DPURoundSte
     elif impl_type == FloatToIntImplType.LEARNED_ROUND:
         return LearnedRoundSte
+    elif impl_type == FloatToIntImplType.STOCHASTIC_ROUND:
+        return StochasticRoundSte
     else:
         raise Exception(f"{impl_type} not recognized.")
 
diff --git a/src/brevitas/utils/quant_utils.py b/src/brevitas/utils/quant_utils.py
index 5d3f2fcd2..5afed36c0 100644
--- a/src/brevitas/utils/quant_utils.py
+++ b/src/brevitas/utils/quant_utils.py
@@ -43,6 +43,13 @@ def float_to_int_impl_to_enum(module):
     elif isinstance(module, CeilSte):
         return FloatToIntImplType.CEIL
     elif isinstance(module, DPURoundSte):
-        return DPURoundSte
+        return FloatToIntImplType.DPU
+    elif isinstance(module, LearnedRoundSte):
+        return FloatToIntImplType.LEARNED_ROUND
+    elif isinstance(module, StochasticRoundSte):
+        if module.deterministic_inference:
+            return FloatToIntImplType.ROUND
+        else:
+            return FloatToIntImplType.STOCHASTIC_ROUND
     else:
         return None