diff --git a/src/brevitas/core/scaling/pre_scaling.py b/src/brevitas/core/scaling/pre_scaling.py
index 6f9a1905a..dd125396d 100644
--- a/src/brevitas/core/scaling/pre_scaling.py
+++ b/src/brevitas/core/scaling/pre_scaling.py
@@ -14,7 +14,7 @@
 from brevitas.core.stats import SCALAR_SHAPE
 from brevitas.core.stats.stats_wrapper import _Stats
 from brevitas.function import abs_binary_sign_grad
-from brevitas.nn.utils import get_upper_bound_on_l1_norm
+from brevitas.function import get_upper_bound_on_l1_norm
 
 __all__ = ["ParameterPreScalingWeightNorm", "AccumulatorAwareParameterPreScaling"]
 
diff --git a/src/brevitas/function/ops.py b/src/brevitas/function/ops.py
index f68ae9ede..feb20ac38 100644
--- a/src/brevitas/function/ops.py
+++ b/src/brevitas/function/ops.py
@@ -201,3 +201,17 @@ def max_float(exponent_bit_width: Tensor, mantissa_bit_width: Tensor, exponent_b
             device=mantissa_bit_width.device)))
     max_val = max_mantissa * (2 ** max_exponent)
     return max_val
+
+
+def get_upper_bound_on_l1_norm(
+        accumulator_bit_width: int, input_bit_width: Tensor, input_is_signed: bool) -> Tensor:
+    """Calculate the upper bound on the l1-norm of the weights using the derivations from
+    `Quantized Neural Networks for Low-Precision Accumulation with Guaranteed Overflow Avoidance`
+    by I.Colbert, A.Pappalardo, and J.Petri-Koenig."""
+    assert input_bit_width is not None, "A2Q relies on input bit-width."
+    assert input_is_signed is not None, "A2Q relies on input sign."
+    input_is_signed = float(input_is_signed)  # 1. if signed else 0.
+    max_accumulator_bit_width = accumulator_bit_width  # P
+    max_accumulator_mag = pow(2., max_accumulator_bit_width - 1.) - 1.  # 2^{P-1}-1
+    max_input_mag_inverse = pow(2., input_is_signed - input_bit_width)
+    return max_accumulator_mag * max_input_mag_inverse
diff --git a/src/brevitas/graph/gpfq.py b/src/brevitas/graph/gpfq.py
index 8d0273856..d4a608156 100644
--- a/src/brevitas/graph/gpfq.py
+++ b/src/brevitas/graph/gpfq.py
@@ -1,21 +1,19 @@
 # Copyright (C) 2023, Advanced Micro Devices, Inc. All rights reserved.
 # SPDX-License-Identifier: BSD-3-Clause
 
-from abc import ABC
 from copy import deepcopy
 from typing import List, Optional
 
 import numpy as np
 import torch
-from torch import Tensor
 import unfoldNd
 
+from brevitas.function import get_upper_bound_on_l1_norm
 from brevitas.graph.gpxq import GPxQ
 from brevitas.graph.gpxq import gpxq_mode
 from brevitas.graph.gpxq import StopFwdException
 from brevitas.graph.gpxq import SUPPORTED_CONV_OP
 import brevitas.nn as qnn
-from brevitas.nn.utils import get_upper_bound_on_l1_norm
 
 
 class gpfq_mode(gpxq_mode):
diff --git a/src/brevitas/nn/utils.py b/src/brevitas/nn/utils.py
index 634f2d751..3e7b423ee 100644
--- a/src/brevitas/nn/utils.py
+++ b/src/brevitas/nn/utils.py
@@ -127,17 +127,3 @@ def calculate_min_accumulator_bit_width(
     min_bit_width = alpha + phi(alpha) + 1.
     min_bit_width = ceil_ste(min_bit_width)
     return min_bit_width  # returns the minimum accumulator that can be used without risk of overflow
-
-
-def get_upper_bound_on_l1_norm(
-        accumulator_bit_width: int, input_bit_width: Tensor, input_is_signed: bool) -> Tensor:
-    """Calculate the upper bound on the l1-norm of the weights using the derivations from
-    `Quantized Neural Networks for Low-Precision Accumulation with Guaranteed Overflow Avoidance`
-    by I.Colbert, A.Pappalardo, and J.Petri-Koenig."""
-    assert input_bit_width is not None, "A2Q relies on input bit-width."
-    assert input_is_signed is not None, "A2Q relies on input sign."
-    input_is_signed = float(input_is_signed)  # 1. if signed else 0.
-    max_accumulator_bit_width = accumulator_bit_width  # P
-    max_accumulator_mag = pow(2., max_accumulator_bit_width - 1.) - 1.  # 2^{P-1}-1
-    max_input_mag_inverse = pow(2., input_is_signed - input_bit_width)
-    return max_accumulator_mag * max_input_mag_inverse