diff --git a/tests/end2end/test_end2end_fifosizing.py b/tests/fpgadataflow/test_fifosizing.py
similarity index 56%
rename from tests/end2end/test_end2end_fifosizing.py
rename to tests/fpgadataflow/test_fifosizing.py
index 32662672300d639a4aadf9b7bde0f5a992217b7e..d93c5630dbd1a4a1d451b9754656df2f4e999309 100644
--- a/tests/end2end/test_end2end_fifosizing.py
+++ b/tests/fpgadataflow/test_fifosizing.py
@@ -28,76 +28,105 @@
 
 import pkg_resources as pk
 
+import pytest
+
+import json
 import numpy as np
+import shutil
 from qonnx.custom_op.registry import getCustomOp
+from qonnx.transformation.general import GiveUniqueNodeNames
 
 import finn.builder.build_dataflow as build
 import finn.builder.build_dataflow_config as build_cfg
 from finn.analysis.fpgadataflow.dataflow_performance import dataflow_performance
 from finn.transformation.fpgadataflow.derive_characteristic import DeriveCharacteristic
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
+from finn.transformation.fpgadataflow.insert_dwc import InsertDWC
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
 from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
 from finn.util.basic import make_build_dir
 
 
 def custom_step_fifosize(model, cfg):
     # TODO convert to NodeLocalTransformation
-    def accumulate_char_fxn(chrc):
-        p = len(chrc)
-        ret = []
-        for t in range(2 * p):
-            if t == 0:
-                ret.append(chrc[0])
-            else:
-                ret.append(ret[-1] + chrc[t % p])
-        return ret
-
     # TODO handle chrc for input and output nodes
     all_act_tensors = [x.name for x in model.graph.value_info]
     for tensor_nm in all_act_tensors:
         # generate accumulated characteristic functions
-        prod = getCustomOp(model.find_producer(tensor_nm))
+        prod = model.find_producer(tensor_nm)
+        cons = model.find_consumer(tensor_nm)
+        if prod is None or cons is None:
+            continue
+        prod = getCustomOp(prod)
+        period = prod.get_nodeattr("io_characteristic_period")
         prod_chrc = prod.get_nodeattr("io_characteristic")
-        prod_chrc = np.asarray(prod_chrc, dtype=np.uint8).reshape(2, -1)[1]
-        prod_chrc = accumulate_char_fxn(prod_chrc)
-        cons = getCustomOp(model.find_consumer(tensor_nm))
+        prod_chrc = np.asarray(prod_chrc).reshape(2, -1)[1]
+        cons = getCustomOp(cons)
         cons_chrc = cons.get_nodeattr("io_characteristic")
-        cons_chrc = np.asarray(cons_chrc, dtype=np.uint8).reshape(2, -1)[0]
-        cons_chrc = accumulate_char_fxn(cons_chrc)
-        # TODO find minimum phase shift
-
-    for node in model.graph.node:
-        inst = getCustomOp(node)
-        chrc = inst.get_nodeattr("io_characteristic")
-        chrc = np.asarray(chrc, dtype=np.uint8).reshape(2, -1)
-
+        cons_chrc = np.asarray(cons_chrc).reshape(2, -1)[0]
+        # find minimum phase shift satisfying the constraint
+        pshift_min = period
+        for pshift_cand in range(period):
+            pshift_condition = [
+                (prod_chrc[i + pshift_cand] >= cons_chrc[i])
+                for i in range(period - pshift_cand)
+            ]
+            if all(pshift_condition):
+                pshift_min = pshift_cand
+                break
+        fifo_depth = max(
+            [(prod_chrc[i + pshift_cand] - cons_chrc[i]) for i in range(pshift_min)]
+        )
+        prod.set_nodeattr("outFIFODepth", fifo_depth)
+        cons.set_nodeattr("inFIFODepth", fifo_depth)
     return model
 
 
 def custom_step_fifocharacterize(model, cfg):
+    model = model.transform(InsertDWC())
+    model = model.transform(GiveUniqueNodeNames())
+    model = model.transform(
+        PrepareIP(cfg._resolve_fpga_part(), cfg._resolve_hls_clk_period())
+    )
+    model = model.transform(HLSSynthIP())
     model = model.transform(PrepareRTLSim())
     period = model.analysis(dataflow_performance)["max_cycles"] + 10
     model = model.transform(DeriveCharacteristic(period))
     return model
 
 
-def test_end2end_fifosizing():
+@pytest.mark.slow
+@pytest.mark.vivado
+def test_fifosizing():
     chkpt_name = pk.resource_filename("finn.qnn-data", "build_dataflow/model.onnx")
     tmp_output_dir = make_build_dir("build_fifosizing_")
-    # tmp_output_dir = "/tmp/finn_dev_maltanar/build_fifosizing_5mt0o6s_"
     steps = build_cfg.default_build_dataflow_steps
-    steps = steps[:10]
-    steps.append(custom_step_fifocharacterize)
-    # steps.append(custom_step_fifosize)
+    steps.insert(10, custom_step_fifocharacterize)
+    steps.insert(11, custom_step_fifosize)
     cfg = build_cfg.DataflowBuildConfig(
         output_dir=tmp_output_dir,
         auto_fifo_depths=False,
         target_fps=10000,
         synth_clk_period_ns=10.0,
         board="Pynq-Z1",
+        rtlsim_batch_size=100,
         shell_flow_type=build_cfg.ShellFlowType.VIVADO_ZYNQ,
-        generate_outputs=[],
+        generate_outputs=[
+            build_cfg.DataflowOutputType.ESTIMATE_REPORTS,
+            build_cfg.DataflowOutputType.STITCHED_IP,
+            build_cfg.DataflowOutputType.RTLSIM_PERFORMANCE,
+        ],
         steps=steps,
-        default_mem_mode=build_cfg.ComputeEngineMemMode.CONST,
-        start_step="custom_step_fifocharacterize",
+        default_mem_mode=build_cfg.ComputeEngineMemMode.DECOUPLED,
     )
     build.build_dataflow_cfg(chkpt_name, cfg)
+    with open(tmp_output_dir + "/report/estimate_network_performance.json") as f:
+        est_data = json.load(f)
+    with open(tmp_output_dir + "/report/rtlsim_performance.json") as f:
+        sim_data = json.load(f)
+    assert (
+        float(sim_data["throughput[images/s]"])
+        / float(est_data["estimated_throughput_fps"])
+        > 0.9
+    )
+    shutil.rmtree(tmp_output_dir)