diff --git a/src/brevitas/core/scaling/pre_scaling.py b/src/brevitas/core/scaling/pre_scaling.py
index 396b747fa..632242507 100644
--- a/src/brevitas/core/scaling/pre_scaling.py
+++ b/src/brevitas/core/scaling/pre_scaling.py
@@ -15,8 +15,7 @@
 from brevitas.core.stats.stats_wrapper import _Stats
 from brevitas.function import abs_binary_sign_grad
 
-__all__ = [
-    "ParameterPreScalingWeightNorm",]
+__all__ = ["ParameterPreScalingWeightNorm", "AccumulatorAwareParameterPreScaling"]
 
 
 class ParameterPreScalingWeightNorm(brevitas.jit.ScriptModule):
diff --git a/src/brevitas/quant/base.py b/src/brevitas/quant/base.py
index 3c82a3d5e..f72045dab 100644
--- a/src/brevitas/quant/base.py
+++ b/src/brevitas/quant/base.py
@@ -352,7 +352,7 @@ def scaling_init(scaling_init_impl, bit_width):
     tensor_clamp_impl = TensorClamp
     scaling_impl = ParameterScaling
     scaling_init_impl = StatsFromParameterScaling
-    restrict_scaling_impl = FloatRestrictValue
+    restrict_scaling_impl = LogFloatRestrictValue
     scaling_stats_impl = AbsMax
     pre_scaling_impl = ParameterPreScalingWeightNorm
     restrict_pre_scaling_impl = LogFloatRestrictValue
@@ -395,7 +395,6 @@ def accumulator_bit_width_impl(accumulator_bit_width):
     proxy_class = DecoupledWeightQuantWithInputProxyFromInjector
     tensor_quant = DecoupledRescalingIntQuantWithInput
     pre_scaling_impl = AccumulatorAwareParameterPreScaling
-    pre_scaling_min_val = 1e-8
     accumulator_bit_width = 32  # default maximum accumulator width is 32 bits
     normalize_stats_impl = L1Norm  # required to align with derivations in paper
     float_to_int_impl = RoundToZeroSte  # required to ensure no upwards rounding violates constraints
diff --git a/src/brevitas_examples/super_resolution/models/common.py b/src/brevitas_examples/super_resolution/models/common.py
index d3022d089..16ba143c5 100644
--- a/src/brevitas_examples/super_resolution/models/common.py
+++ b/src/brevitas_examples/super_resolution/models/common.py
@@ -6,6 +6,7 @@
 from torch import Tensor
 import torch.nn as nn
 
+from brevitas.core.restrict_val import FloatRestrictValue
 from brevitas.core.restrict_val import RestrictValueType
 from brevitas.core.scaling import ScalingImplType
 import brevitas.nn as qnn
@@ -25,6 +26,7 @@ class CommonIntWeightPerChannelQuant(Int8WeightPerTensorFloat):
 
 
 class CommonIntAccumulatorAwareWeightQuant(Int8AccumulatorAwareWeightQuant):
+    restrict_scaling_impl = FloatRestrictValue  # backwards compatibility
     pre_scaling_min_val = 1e-10
     scaling_min_val = 1e-10