From f60e4abe943dac75a03fb1824c4fb02e700cfb26 Mon Sep 17 00:00:00 2001
From: icolbert <Ian.Colbert@amd.com>
Date: Tue, 21 Feb 2023 09:14:54 -0800
Subject: [PATCH] Inline lambda function for data type bound
---
src/finn/custom_op/fpgadataflow/matrixvectoractivation.py | 6 +++---
src/finn/custom_op/fpgadataflow/vectorvectoractivation.py | 6 +++---
2 files changed, 6 insertions(+), 6 deletions(-)
diff --git a/src/finn/custom_op/fpgadataflow/matrixvectoractivation.py b/src/finn/custom_op/fpgadataflow/matrixvectoractivation.py
index c440b3675..6aa26af45 100644
--- a/src/finn/custom_op/fpgadataflow/matrixvectoractivation.py
+++ b/src/finn/custom_op/fpgadataflow/matrixvectoractivation.py
@@ -353,12 +353,12 @@ class MatrixVectorActivation(HLSCustomOp):
acc_datatype = self.get_accumulator_datatype()
# if accDataType is not set, then it will default to INT32, which would
# be a large overestimate in most (if not all) cases. In this scenario,
- # we would use the minimum accumulator as determined by the data types.
+ # we would use the minimum accumulator as determined by the data types
+ # bound, derived in https://arxiv.org/abs/2301.13376
alpha = math.log(MW, 2) + W + A - 1 - int(idt.signed())
- phi = lambda x_: math.log(1 + pow(2, -x_), 2)
acc_bits = min(
acc_datatype.bitwidth(),
- np.ceil(alpha + phi(alpha) + 1)
+ np.ceil(alpha + math.log(1 + pow(2, -alpha), 2) + 1)
)
acc_luts = acc_bits
# thresholds and threshold comparators
diff --git a/src/finn/custom_op/fpgadataflow/vectorvectoractivation.py b/src/finn/custom_op/fpgadataflow/vectorvectoractivation.py
index 377a62f79..796225a71 100644
--- a/src/finn/custom_op/fpgadataflow/vectorvectoractivation.py
+++ b/src/finn/custom_op/fpgadataflow/vectorvectoractivation.py
@@ -1199,12 +1199,12 @@ class VectorVectorActivation(HLSCustomOp):
k_h, k_w = self.get_nodeattr("Kernel")
# if accDataType is not set, then it will default to INT32, which would
# be a large overestimate in most (if not all) cases. In this scenario,
- # we would use the minimum accumulator as determined by the data types.
+ # we would use the minimum accumulator as determined by the data types
+ # bound, derived in https://arxiv.org/abs/2301.13376
alpha = math.log(k_h * k_w, 2) + W + A - 1 - int(idt.signed())
- phi = lambda x_: math.log(1 + pow(2, -x_), 2)
acc_bits = min(
acc_datatype.bitwidth(),
- np.ceil(alpha + phi(alpha) + 1)
+ np.ceil(alpha + math.log(1 + pow(2, -alpha), 2) + 1)
)
acc_luts = acc_bits
# thresholds and threshold comparators
--
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