From 1f2488aecbf9f7ae0169943327639879fc2987b5 Mon Sep 17 00:00:00 2001
From: Hendrik Borras <hendrikborras@web.de>
Date: Wed, 23 Jun 2021 12:23:45 +0100
Subject: [PATCH] Added SIMD requirement to StreamingFCLayer_Batch
---
.../fpgadataflow/streamingfclayer_batch.py | 41 +++++++++++++------
1 file changed, 29 insertions(+), 12 deletions(-)
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index 48b40f105..1e4dc0b18 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -238,9 +238,10 @@ class StreamingFCLayer_Batch(HLSCustomOp):
mem_width = Q * W * P
mmode = self.get_nodeattr("mem_mode")
mstyle = self.get_nodeattr("ram_style")
- if (mmode == "decoupled" and mstyle != "ultra") or (
- mmode == "const" and self.calc_wmem() <= 128) or (
- mmode == "external"
+ if (
+ (mmode == "decoupled" and mstyle != "ultra")
+ or (mmode == "const" and self.calc_wmem() <= 128)
+ or (mmode == "external")
):
return 0
width_multiplier = math.ceil(mem_width / 72)
@@ -266,9 +267,10 @@ class StreamingFCLayer_Batch(HLSCustomOp):
mem_width = Q * W * P
mmode = self.get_nodeattr("mem_mode")
mstyle = self.get_nodeattr("ram_style")
- if (mmode == "decoupled" and mstyle in ["distributed", "ultra"]) or (
- mmode == "const" and self.calc_wmem() <= 128) or (
- mmode == "external"
+ if (
+ (mmode == "decoupled" and mstyle in ["distributed", "ultra"])
+ or (mmode == "const" and self.calc_wmem() <= 128)
+ or (mmode == "external")
):
return 0
# assuming SDP mode RAMB18s (see UG573 Table 1-10)
@@ -604,9 +606,11 @@ class StreamingFCLayer_Batch(HLSCustomOp):
tdt = DataType.get_smallest_possible(0 - tdt_max)
else:
tdt = DataType.get_smallest_possible(tdt_max)
- assert np.vectorize(tdt.allowed)(threshold_tensor).all(), (
- "Thresholds in %s can't be expressed with type %s"
- % (self.onnx_node.name, str(tdt))
+ assert np.vectorize(tdt.allowed)(
+ threshold_tensor
+ ).all(), "Thresholds in %s can't be expressed with type %s" % (
+ self.onnx_node.name,
+ str(tdt),
)
self.set_nodeattr("accDataType", tdt.name)
else:
@@ -843,9 +847,11 @@ class StreamingFCLayer_Batch(HLSCustomOp):
# get computed threshold datatype from attribute
tdt = DataType[self.get_nodeattr("accDataType")]
- assert np.vectorize(tdt.allowed)(threshold_tensor).all(), (
- "Thresholds in %s can't be expressed with type %s"
- % (self.onnx_node.name, str(tdt))
+ assert np.vectorize(tdt.allowed)(
+ threshold_tensor
+ ).all(), "Thresholds in %s can't be expressed with type %s" % (
+ self.onnx_node.name,
+ str(tdt),
)
thresholds_hls_code = numpy_to_hls_code(
threshold_tensor, tdt, "thresholds", False, True
@@ -1005,6 +1011,17 @@ class StreamingFCLayer_Batch(HLSCustomOp):
self.code_gen_dict["$GLOBALS$"] += ['#include "thresh.h"']
def defines(self, var):
+ # Only ipgen mode: Make sure that SIMD parameter satisfies minimum requirements.
+ if var == "ipgen":
+ SIMD = self.get_nodeattr("SIMD")
+ MW = self.get_nodeattr("MW")
+ condition = SIMD > (MW / 1024)
+ msg = (
+ f"HLS synthesis of StreamingFCLayer_Batch requires: "
+ f"SIMD > MW / 1024. This is not fulfilled with: SIMD={SIMD} "
+ f"and MW={MW} for node: {self.onnx_node.name}."
+ )
+ assert condition, msg
mem_mode = self.get_nodeattr("mem_mode")
numInputVectors = list(self.get_nodeattr("numInputVectors"))
numReps = np.prod(numInputVectors)
--
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