Merge pull request #1110 from Xilinx/feature/per_tensor_rtl_thresh

Optimizing resources for per tensor quantization

src/finn/custom_op/fpgadataflow/rtl/thresholding_rtl.py

@@ -205,9 +205,9 @@ def prepare_codegen_rtl_values(self, model):
         num_channels = self.get_nodeattr("NumChannels")  # number of channels
 
         # If a single threshold value is found, broadcast the value
-        expected_shape = (num_channels, expected_thresholds)
-        if t_packed.shape != expected_shape:
-            t_packed = np.broadcast_to(t_packed, expected_shape)
+        if t_packed.shape[0] == 1:
+            t_packed = np.broadcast_to(t_packed, (pe, expected_thresholds))
+            num_channels = pe
 
         channel_fold = int(num_channels / pe)
 
@@ -531,11 +531,11 @@ def make_weight_file(self, weights, weight_file_mode, weight_file_name):
             min_val = wdt.min()
             thresholds = np.insert(thresholds, 0, min_val, axis=1)
             n_thres_steps += 1
-        expected_shape = (ch, expected_thresholds)
 
         # If a single threshold value is found, broadcast the value
-        if thresholds.shape != expected_shape:
-            thresholds = np.broadcast_to(thresholds, expected_shape)
+        if thresholds.shape[0] == 1:
+            thresholds = np.broadcast_to(thresholds, (pe, expected_thresholds))
+            ch = pe
 
         width_padded = roundup_to_integer_multiple(thresholds.shape[1], 2**o_bitwidth)
         thresh_padded = np.zeros((thresholds.shape[0], width_padded))