diff --git a/src/finn/custom_op/fpgadataflow/__init__.py b/src/finn/custom_op/fpgadataflow/__init__.py
index a688898f4a43b33fd3f07cda12144b84829e451f..14ba1b813c8d61145f7d221deee9c184aeb9bddc 100644
--- a/src/finn/custom_op/fpgadataflow/__init__.py
+++ b/src/finn/custom_op/fpgadataflow/__init__.py
@@ -171,9 +171,15 @@ class HLSCustomOp(CustomOp):
         of the node as a dictionary."""
         ret = dict()
         ret["BRAM_18K"] = self.bram_estimation()
+        ret["BRAM_efficiency"] = self.bram_efficiency_estimation()
         ret["LUT"] = self.lut_estimation()
         return ret
 
+    def bram_efficiency_estimation(self):
+        """Function for BRAM efficiency estimation: actual parameter storage
+        needed divided by the allocated BRAM storage (from estimation)"""
+        return 1
+
     def bram_estimation(self):
         """Function for BRAM resource estimation, is member function of
         HLSCustomOp class but has to be filled by every node"""
@@ -219,7 +225,6 @@ class HLSCustomOp(CustomOp):
         self.code_gen_dict["$CLKPERIOD$"] = [str(clk)]
         self.code_gen_dict["$EXTRA_DIRECTIVES$"] = self.ipgen_extra_directives()
 
-
         template = self.ipgentcl_template
 
         for key in self.code_gen_dict:
@@ -235,7 +240,7 @@ class HLSCustomOp(CustomOp):
     def ipgen_extra_directives(self):
         "Return a list of extra tcl directives for HLS synthesis."
         return []
-        
+
     def ipgen_singlenode_code(self):
         """Builds the bash script for ip generation using the IPGenBuilder from
         finn.util.fpgadataflow."""
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index e8f54dc082cfe8c0d0374446945869c9f283cd51..a7ebff68749120868cae9ce5ac18d2856fe2cb8a 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -245,6 +245,16 @@ class StreamingFCLayer_Batch(HLSCustomOp):
         omega = (D_in * D_out) / (Q * P)
         return P * (math.ceil(omega / 512)) * (math.ceil((Q * W) / 36))
 
+    def bram_efficiency_estimation(self):
+        wdt = self.get_weight_datatype()
+        W = wdt.bitwidth()
+        D_in = self.get_nodeattr("MW")
+        D_out = self.get_nodeattr("MH")
+        bram16_est = self.bram_estimation()
+        wbits = W * D_in * D_out
+        bram16_est_capacity = bram16_est * 36 * 512
+        return wbits / bram16_est_capacity
+
     def lut_estimation(self):
         """Calculates resource estimations for LUTs based on:
         - FINN-R: An End-to-End Deep-Learning Framework for Fast
diff --git a/src/finn/transformation/fpgadataflow/annotate_resources.py b/src/finn/transformation/fpgadataflow/annotate_resources.py
index 207075b00de1871da19ea78472125d435449ed6e..62ee92df54eee2b63d84657515d7fbc3a8808b81 100644
--- a/src/finn/transformation/fpgadataflow/annotate_resources.py
+++ b/src/finn/transformation/fpgadataflow/annotate_resources.py
@@ -69,6 +69,9 @@ class AnnotateResources(Transformation):
                     total_dict[r_type] += r_amount
                 else:
                     total_dict[r_type] = r_amount
+        for k in total_dict.keys():
+            if "efficiency" in k:
+                total_dict[k] = total_dict[k] / len(graph.node)
         model.set_metadata_prop("res_total_" + self.mode, str(total_dict))
         for node in graph.node:
             if _is_fpgadataflow_node(node) and node.name in res_dict.keys():