fix Neureka DW weight encoding and parsing

Author: marchioa

Deeploy/Targets/Neureka/Engine.py

@@ -31,12 +31,14 @@
         ConvLayer([NeurekaPWConv2DMapper, NeurekaDWConv2DMapper, NeurekaDenseConv2DMapper]),
 }
 
-_includeList = ["pulp_nnx_neureka.h", "pulp_nnx_util.h", "neureka_siracusa_bsp.h", "neureka.h", "neureka_task.h", "neureka_gvsoc.h"]
+_includeList = [
+    "pulp_nnx_neureka.h", "pulp_nnx_util.h", "neureka_siracusa_bsp.h", "neureka.h", "neureka_task.h", "neureka_gvsoc.h"
+]
 
 _neurekaInitCode = r"""
 neureka_siracusa_conf_t conf = {.max_stall = 8};
 neureka_nnx_init(neureka_siracusa_get_dev(), &conf);
-neureka_gvsoc_log_activate(neureka_siracusa_get_dev(), NEUREKA_GVSOC_LOG_LEVEL_ALL, NEUREKA_GVSOC_LOG_FORMAT_DECIMAL);
+neureka_gvsoc_log_activate(neureka_siracusa_get_dev(), NEUREKA_GVSOC_LOG_LEVEL_ALL, NEUREKA_GVSOC_LOG_FORMAT_HEXADECIMAL);
 """
 
 

Deeploy/Targets/Neureka/Parsers.py

@@ -87,13 +87,19 @@ def parseNode(self, node: gs.Node) -> bool:
         if not super().parseNode(node):
             return False
 
-        ch_im_out = node.inputs[1].shape[0]
-        ch_im_in = node.inputs[1].shape[1]
-
+        weights = node.inputs[1]
+
+        # weigths reshaped by the weigths encoder into
+        # (cout, cinMajor, bits, weightBandwidthBytes)
+        # where:
+        # - cout: 1 by definition (it is cin from ONNX)
+        # - cinMajor: number of tiles over the channels
+        # - bits: weight bit width (only 8 is supported)
+        # - weightBandwidthBytes: which is 32 in Siracusa
         if not all([
                 self.operatorRepresentation['kernel_shape'] == [3, 3],
-                self.operatorRepresentation['group'] == ch_im_out,
-                ch_im_in == 1,
+                len(weights.shape) == 4,
+                weights.shape[0] == 1,  # ch_im_out
         ]):
             return False
 

Deeploy/Targets/Neureka/TopologyOptimizationPasses/Passes.py

@@ -34,9 +34,6 @@ def _weightEncode(weight: npt.NDArray[np.uint8], bits: int, depthwise: bool = Fa
     _NEUREKA_CIN_SUBTILE_1x1 = 32
     _NEUREKA_CIN_SUBTILE_3x3 = 28
 
-    if depthwise:
-        weight = weight.transpose(1, 0, 2, 3)  # Swap cout and cin
-
     cout, cin, height, width = weight.shape
     cinSubtile = (_NEUREKA_CIN_SUBTILE_3x3 if height == 3 else _NEUREKA_CIN_SUBTILE_1x1)