From d222db36ee8e740931def8302a7b8e099fe18fbf Mon Sep 17 00:00:00 2001
From: icolbert <Ian.Colbert@amd.com>
Date: Thu, 1 Dec 2022 08:01:06 -0800
Subject: [PATCH] Updating MVAU LUT estimation
- Using accDataType rather than an estimate
- Updated the estimate equation for case when accDataType is not specified
- Adding logic check that thresholds are also using LUTRAM rather than BRAM
---
.../fpgadataflow/matrixvectoractivation.py | 18 ++++++++++++++++--
1 file changed, 16 insertions(+), 2 deletions(-)
diff --git a/src/finn/custom_op/fpgadataflow/matrixvectoractivation.py b/src/finn/custom_op/fpgadataflow/matrixvectoractivation.py
index df9d1f1e7..ed19b93bb 100644
--- a/src/finn/custom_op/fpgadataflow/matrixvectoractivation.py
+++ b/src/finn/custom_op/fpgadataflow/matrixvectoractivation.py
@@ -350,13 +350,23 @@ class MatrixVectorActivation(HLSCustomOp):
# adder tree
addertree_luts = (W + A) * (2 * Q - 1)
# accumulator
- acc_bits = W + A + np.ceil(math.log(MW, 2))
+ 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.
+ 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)
+ )
acc_luts = acc_bits
# thresholds and threshold comparators
thr_luts = 0
comp_luts = 0
noact = self.get_nodeattr("noActivation")
- if noact == 0:
+ tmem_style = self.get_nodeattr("ram_style_thresholds")
+ if (noact == 0) and (tmem_style == "distributed"):
odt = self.get_output_datatype()
B = odt.bitwidth()
thr_luts = (2**B - 1) * acc_bits * math.ceil(self.calc_tmem() / 64)
@@ -405,6 +415,10 @@ class MatrixVectorActivation(HLSCustomOp):
else:
raise Exception("Undefined input ind for this layer type")
+ def get_accumulator_datatype(self):
+ """Returns FINN DataType of accumulator"""
+ return DataType[self.get_nodeattr("accDataType")]
+
def get_weight_datatype(self):
"""Returns FINN DataType of weights."""
return DataType[self.get_nodeattr("weightDataType")]
--
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