Merge pull request #965 from iksnagreb/feature/post-synth-resources

Enable analysis report of post synthesis resource utilization in json

src/finn/analysis/fpgadataflow/post_synth_res.py

@@ -58,14 +58,13 @@ def post_synth_res(model, override_synth_report_filename=None):
     else:
         raise Exception("Please run synthesis first")
 
-    # TODO build these indices based on table headers instead of harcoding
     restype_to_ind_default = {
         "LUT": 2,
         "SRL": 5,
         "FF": 6,
         "BRAM_36K": 7,
         "BRAM_18K": 8,
-        "DSP48": 9,
+        "DSP": 10,
     }
     restype_to_ind_vitis = {
         "LUT": 4,
@@ -74,13 +73,36 @@ def post_synth_res(model, override_synth_report_filename=None):
         "BRAM_36K": 9,
         "BRAM_18K": 10,
         "URAM": 11,
-        "DSP48": 12,
+        "DSP": 12,
     }
 
-    if model.get_metadata_prop("platform") == "alveo":
-        restype_to_ind = restype_to_ind_vitis
+    # format: (human_readable_name_in_report, canonical_name)
+    res_types_to_search = [
+        ("Total LUTs", "LUT"),
+        ("SRLs", "SRL"),
+        ("FFs", "FF"),
+        ("RAMB36", "BRAM_36K"),
+        ("RAMB18", "BRAM_18K"),
+        ("URAM", "URAM"),
+        ("DSP Blocks", "DSP"),
+    ]
+
+    # try to infer resource type to table index by
+    # looking at the names in headings
+    header_row = root.findall(".//*[@contents='Instance']/..")
+    if header_row != []:
+        headers = [x.attrib["contents"] for x in list(header_row[0])]
+        restype_to_ind = {}
+        for res_type_name, res_type in res_types_to_search:
+            if res_type_name in headers:
+                restype_to_ind[res_type] = headers.index(res_type_name)
     else:
-        restype_to_ind = restype_to_ind_default
+        # could not infer resource types from header
+        # fall back to default indices
+        if model.get_metadata_prop("platform") == "alveo":
+            restype_to_ind = restype_to_ind_vitis
+        else:
+            restype_to_ind = restype_to_ind_default
 
     def get_instance_stats(inst_name):
         row = root.findall(".//*[@contents='%s']/.." % inst_name)

src/finn/builder/build_dataflow_steps.py

@@ -62,6 +62,7 @@
     aggregate_dict_keys,
     op_and_param_counts,
 )
+from finn.analysis.fpgadataflow.post_synth_res import post_synth_res
 from finn.analysis.fpgadataflow.res_estimation import (
     res_estimation,
     res_estimation_complete,
@@ -801,6 +802,11 @@ def step_synthesize_bitfile(model: ModelWrapper, cfg: DataflowBuildConfig):
                 model.get_metadata_prop("vivado_synth_rpt"),
                 report_dir + "/post_synth_resources.xml",
             )
+
+            post_synth_resources = model.analysis(post_synth_res)
+            with open(report_dir + "/post_synth_resources.json", "w") as f:
+                json.dump(post_synth_resources, f, indent=2)
+
             vivado_pynq_proj_dir = model.get_metadata_prop("vivado_pynq_proj")
             timing_rpt = (
                 "%s/finn_zynq_link.runs/impl_1/top_wrapper_timing_summary_routed.rpt"
@@ -825,6 +831,10 @@ def step_synthesize_bitfile(model: ModelWrapper, cfg: DataflowBuildConfig):
                 model.get_metadata_prop("vivado_synth_rpt"),
                 report_dir + "/post_synth_resources.xml",
             )
+
+            post_synth_resources = model.analysis(post_synth_res)
+            with open(report_dir + "/post_synth_resources.json", "w") as f:
+                json.dump(post_synth_resources, f, indent=2)
         else:
             raise Exception("Unrecognized shell_flow_type: " + str(cfg.shell_flow_type))
         print("Bitfile written into " + bitfile_dir)

tests/util/test_build_dataflow.py

@@ -64,6 +64,7 @@ def test_end2end_build_dataflow_directory():
     assert os.path.isfile(output_dir + "/bitfile/finn-accel.hwh")
     assert os.path.isfile(output_dir + "/report/post_synth_resources.xml")
     assert os.path.isfile(output_dir + "/report/post_route_timing.rpt")
+    assert os.path.isfile(output_dir + "/report/post_synth_resources.json")
     # verification outputs
     verif_batchsize = np.load(target_dir + "/input.npy").shape[0]
     for i in range(verif_batchsize):