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Fix VisualWakeWords on GAP9 #187

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65eda17
Fix DWConv im2col scratch buffer sizing on PULPOpen
Xeratec Apr 30, 2026
0e0d596
Fix pulp-nn linear leftover-neuron RQS parameter aliasing
Xeratec Apr 30, 2026
06e2250
Enable VisualWakeWord Test on GAP9
Xeratec Apr 30, 2026
2519c50
Fix transient-buffer hoisting to dispatch through subclass overrides
Xeratec Apr 30, 2026
8c769c7
Add depthwise im2col override for PULP1DDWConvTemplate
Xeratec Apr 30, 2026
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6 changes: 2 additions & 4 deletions Deeploy/Targets/Generic/Templates/iSoftmaxPreAllocatedBuffTemplate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,9 +11,8 @@

class iSoftmaxPreAllocatedBuffTemplate(NodeTemplate):

@staticmethod
def computeTransientBuffersSize(
ctxt: NetworkContext,
self, ctxt: NetworkContext,
operatorRepresentation: OperatorRepresentation) -> List[Tuple[str, Union[int, IntVar]]]:

lastDimBuffer_dim = 8 * 4 * operatorRepresentation['lastDimLength']
Expand All @@ -22,8 +21,7 @@ def computeTransientBuffersSize(

def hoistTransientBuffers(self, ctxt: NetworkContext,
operatorRepresentation: OperatorRepresentation) -> Tuple[NetworkContext, Dict, List[str]]:
lastDimBuffer_name, lastDimBuffer_dim = iSoftmaxPreAllocatedBuffTemplate.computeTransientBuffersSize(
ctxt, operatorRepresentation)[0]
lastDimBuffer_name, lastDimBuffer_dim = self.computeTransientBuffersSize(ctxt, operatorRepresentation)[0]
ctxt.hoistTransientBuffer(lastDimBuffer_name, lastDimBuffer_dim)

operatorRepresentation['lastDimBuffer'] = lastDimBuffer_name
Expand Down
40 changes: 34 additions & 6 deletions Deeploy/Targets/PULPOpen/Templates/ConvTemplate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,9 +26,8 @@ def alignToContext(self, ctxt: NetworkContext,

return ctxt, operatorRepresentation, []

@staticmethod
def computeTransientBuffersSize(
ctxt: NetworkContext,
self, ctxt: NetworkContext,
operatorRepresentation: OperatorRepresentation) -> List[Tuple[str, Union[int, IntVar]]]:
im2col_dim = 2 * 8 * (operatorRepresentation['ch_im_in'] * operatorRepresentation['dim_kernel_x'] *
operatorRepresentation['dim_kernel_y'])
Expand All @@ -37,7 +36,7 @@ def computeTransientBuffersSize(

def hoistTransientBuffers(self, ctxt: NetworkContext,
operatorRepresentation: OperatorRepresentation) -> Tuple[NetworkContext, Dict, List[str]]:
im2col_name, im2col_dim = PULP2DConvTemplate.computeTransientBuffersSize(ctxt, operatorRepresentation)[0]
im2col_name, im2col_dim = self.computeTransientBuffersSize(ctxt, operatorRepresentation)[0]
ctxt.hoistTransientBuffer(im2col_name, im2col_dim)

operatorRepresentation['ctxtBuffer'] = im2col_name
Expand All @@ -62,6 +61,22 @@ def alignToContext(self, ctxt: NetworkContext,

return ctxt, operatorRepresentation, []

def computeTransientBuffersSize(
self, ctxt: NetworkContext,
operatorRepresentation: OperatorRepresentation) -> List[Tuple[str, Union[int, IntVar]]]:
# The depthwise pulp-nn kernel reuses one column-shaped im2col scratch
# per core. Per core it needs `dim_kernel_x * (dim_in_y + pad_top +
# pad_bot) + dim_kernel_x` bytes (the trailing `+ dim_kernel_x` is the
# safety zone for the last v4u write).
pad_top = operatorRepresentation['padding_y_top']
pad_bot = operatorRepresentation['padding_y_bottom']
per_core = (operatorRepresentation['dim_kernel_x'] *
(operatorRepresentation['dim_im_in_y'] + pad_top + pad_bot) +
operatorRepresentation['dim_kernel_x'])
im2col_dim = 8 * per_core
im2col_name = operatorRepresentation['nodeName'] + "_buffer"
return [(im2col_name, im2col_dim)]


class PULP1DConvTemplate(NodeTemplate):

Expand All @@ -84,17 +99,16 @@ def alignToContext(self, ctxt: NetworkContext,

return ctxt, operatorRepresentation, []

@staticmethod
def computeTransientBuffersSize(
ctxt: NetworkContext,
self, ctxt: NetworkContext,
operatorRepresentation: OperatorRepresentation) -> List[Tuple[str, Union[int, IntVar]]]:
im2col_dim = 8 * 2 * operatorRepresentation['ch_im_in'] * operatorRepresentation['dim_kernel_y']
im2col_name = operatorRepresentation['nodeName'] + "_buffer"
return [(im2col_name, im2col_dim)]

def hoistTransientBuffers(self, ctxt: NetworkContext,
operatorRepresentation: OperatorRepresentation) -> Tuple[NetworkContext, Dict, List[str]]:
im2col_name, im2col_dim = PULP1DConvTemplate.computeTransientBuffersSize(ctxt, operatorRepresentation)[0]
im2col_name, im2col_dim = self.computeTransientBuffersSize(ctxt, operatorRepresentation)[0]
ctxt.hoistTransientBuffer(im2col_name, im2col_dim)
operatorRepresentation['ctxtBuffer'] = im2col_name
operatorRepresentation['ctxtBufferSize'] = im2col_dim
Expand All @@ -106,6 +120,20 @@ class PULP1DDWConvTemplate(PULP1DConvTemplate):
def __init__(self, templateStr):
super().__init__(templateStr)

def computeTransientBuffersSize(
self, ctxt: NetworkContext,
operatorRepresentation: OperatorRepresentation) -> List[Tuple[str, Union[int, IntVar]]]:
# The depthwise pulp-nn kernel reuses one column-shaped im2col scratch
# per core. Per core it needs `dim_kernel_y * (dim_in_y + pad_top + pad_bot) + dim_kernel_y` bytes (the trailing `+ dim_kernel_y` is the safety zone for the last v4u write).
pad_top = operatorRepresentation['padding_y_top']
pad_bot = operatorRepresentation['padding_y_bottom']
per_core = (operatorRepresentation['dim_kernel_y'] *
(operatorRepresentation['dim_im_in_y'] + pad_top + pad_bot) +
operatorRepresentation['dim_kernel_y'])
im2col_dim = 8 * per_core
im2col_name = operatorRepresentation['nodeName'] + "_buffer"
return [(im2col_name, im2col_dim)]


PULPConv2D_8_Template = PULP2DConvTemplate("""
// PULP NN CONV
Expand Down
12 changes: 4 additions & 8 deletions Deeploy/Targets/PULPOpen/Templates/FloatConvTemplate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,9 +14,8 @@ class PULP2DFloatConvIm2ColTemplate(NodeTemplate):
def __init__(self, templateStr):
super().__init__(templateStr)

@staticmethod
def computeTransientBuffersSize(
ctxt: NetworkContext,
self, ctxt: NetworkContext,
operatorRepresentation: OperatorRepresentation) -> List[Tuple[str, Union[int, IntVar]]]:
# Memory allocation for the im2col buffer can be dynamic, based on the number of cores.
im2col_dim = (operatorRepresentation["weight_type"].typeWidth //
Expand All @@ -29,8 +28,7 @@ def computeTransientBuffersSize(

def hoistTransientBuffers(self, ctxt: NetworkContext,
operatorRepresentation: OperatorRepresentation) -> Tuple[NetworkContext, Dict, List[str]]:
im2col_name, im2col_dim = PULP2DFloatConvIm2ColTemplate.computeTransientBuffersSize(
ctxt, operatorRepresentation)[0]
im2col_name, im2col_dim = self.computeTransientBuffersSize(ctxt, operatorRepresentation)[0]
ctxt.hoistTransientBuffer(im2col_name, im2col_dim)

operatorRepresentation['ctxtBuffer'] = im2col_name
Expand All @@ -43,9 +41,8 @@ class PULP2DFloatDWConvIm2ColTemplate(NodeTemplate):
def __init__(self, templateStr):
super().__init__(templateStr)

@staticmethod
def computeTransientBuffersSize(
ctxt: NetworkContext,
self, ctxt: NetworkContext,
operatorRepresentation: OperatorRepresentation) -> List[Tuple[str, Union[int, IntVar]]]:

# Memory allocation for the im2col buffer can be dynamic, based on the number of cores.
Expand All @@ -58,8 +55,7 @@ def computeTransientBuffersSize(

def hoistTransientBuffers(self, ctxt: NetworkContext,
operatorRepresentation: OperatorRepresentation) -> Tuple[NetworkContext, Dict, List[str]]:
im2col_name, im2col_dim = PULP2DFloatDWConvIm2ColTemplate.computeTransientBuffersSize(
ctxt, operatorRepresentation)[0]
im2col_name, im2col_dim = self.computeTransientBuffersSize(ctxt, operatorRepresentation)[0]
ctxt.hoistTransientBuffer(im2col_name, im2col_dim)

# Manually set the type of the im2col buffer to match the input type, since it defaults to void for transient buffers
Expand Down
6 changes: 2 additions & 4 deletions Deeploy/Targets/PULPOpen/Templates/iSoftmaxTemplate.py
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Expand Up @@ -11,9 +11,8 @@

class PULPiSoftmaxTemplate(NodeTemplate):

@staticmethod
def computeTransientBuffersSize(
ctxt: NetworkContext,
self, ctxt: NetworkContext,
operatorRepresentation: OperatorRepresentation) -> List[Tuple[str, Union[int, IntVar]]]:

lastDimBuffer_dim = 8 * 4 * operatorRepresentation['lastDimLength']
Expand All @@ -22,8 +21,7 @@ def computeTransientBuffersSize(

def hoistTransientBuffers(self, ctxt: NetworkContext,
operatorRepresentation: OperatorRepresentation) -> Tuple[NetworkContext, Dict, List[str]]:
lastDimBuffer_name, lastDimBuffer_dim = PULPiSoftmaxTemplate.computeTransientBuffersSize(
ctxt, operatorRepresentation)[0]
lastDimBuffer_name, lastDimBuffer_dim = self.computeTransientBuffersSize(ctxt, operatorRepresentation)[0]
ctxt.hoistTransientBuffer(lastDimBuffer_name, lastDimBuffer_dim)

operatorRepresentation['lastDimBuffer'] = lastDimBuffer_name
Expand Down
Binary file added DeeployTest/Tests/Kernels/Integer/Conv/DW_2D_RQ_8x16x16/activations.npz
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Binary file added DeeployTest/Tests/Kernels/Integer/Conv/DW_2D_RQ_8x16x16/inputs.npz
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Binary file added DeeployTest/Tests/Kernels/Integer/Conv/DW_2D_RQ_8x16x16/network.onnx
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Binary file added DeeployTest/Tests/Kernels/Integer/Conv/DW_2D_RQ_8x16x16/outputs.npz
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6 changes: 5 additions & 1 deletion DeeployTest/test_gap9_tiled_config.py
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Expand Up @@ -13,7 +13,8 @@
"Kernels/Integer/MatMul/Regular": [64000, 32000, 16000],
"Kernels/Integer/Conv/Regular_2D_RQ": [8000, 6000, 4000],
"Kernels/Integer/Conv/StriddedPadded_2D_RQ": [600],
"Kernels/Integer/Conv/DW_2D_RQ": [2561],
"Kernels/Integer/Conv/DW_2D_RQ": [64000, 2561],
"Kernels/Integer/Conv/DW_2D_RQ_8x16x16": [64000],
"Kernels/Integer/Softmax/Regular": [800, 500, 300],
"Kernels/Integer/Concat": [32000, 16000, 8000],
"Kernels/Integer/Hardswish/Regular": [750],
Expand All @@ -39,6 +40,7 @@
"Kernels/Integer/MatMul/Regular": [64000, 32000, 16000],
"Kernels/Integer/Conv/Regular_2D_RQ": [8000, 6000, 5000],
"Kernels/Integer/Conv/DW_2D_RQ": [5121],
"Kernels/Integer/Conv/DW_2D_RQ_8x16x16": [64000],
"Kernels/Integer/Softmax/Regular": [1600, 1000, 600],
"Kernels/Integer/Concat": [64000, 32000, 16000],
"Kernels/Integer/Hardswish/Regular": [750],
Expand All @@ -64,6 +66,7 @@
"Models/MLPerf/KeywordSpotting": [64000],
"Models/MLPerf/ImageClassification": [64000],
"Models/MLPerf/AnomalyDetection": [64000],
"Models/MLPerf/VisualWakeWords": [64000],
}

L2_DOUBLEBUFFER_MODELS = {
Expand All @@ -72,6 +75,7 @@
"Models/MLPerf/KeywordSpotting": [64000],
"Models/MLPerf/ImageClassification": [64000],
"Models/MLPerf/AnomalyDetection": [64000],
"Models/MLPerf/VisualWakeWords": [64000],
}

L3_SINGLEBUFFER_MODELS = {
Expand Down
2 changes: 2 additions & 0 deletions DeeployTest/test_siracusa_tiled_config.py
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Expand Up @@ -112,6 +112,7 @@
"Models/MLPerf/KeywordSpotting": [64000],
"Models/MLPerf/ImageClassification": [64000],
"Models/MLPerf/AnomalyDetection": [64000],
"Models/MLPerf/VisualWakeWords": [64000],
"Models/CCT/FP32/CCT_1_16_16_8": [64000],
"Models/TinyViT/Demo": [4000],
}
Expand All @@ -127,6 +128,7 @@
"Models/MLPerf/KeywordSpotting": [128000],
"Models/MLPerf/ImageClassification": [128000],
"Models/MLPerf/AnomalyDetection": [128000],
"Models/MLPerf/VisualWakeWords": [128000],
"Models/CCT/FP32/CCT_1_16_16_8": [128000],
"Models/TinyViT/Demo": [8000],
}
Expand Down
2 changes: 2 additions & 0 deletions TargetLibraries/GAP9/CMakeLists.txt
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Expand Up @@ -23,6 +23,8 @@ target_compile_options(deeploygap9 PUBLIC
)

target_compile_options(deeploygap9 PRIVATE
-Wno-incompatible-pointer-types
-Wno-implicit-function-declaration
-Wno-sign-conversion
-Wno-sign-compare
-Wno-type-limits
Expand Down
Binary file modified TargetLibraries/GAP9/prebuilt/libpulp-nn-mixed.a
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3 changes: 2 additions & 1 deletion TargetLibraries/PULPOpen/CMakeLists.txt
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Expand Up @@ -62,7 +62,8 @@ if (platform IN_LIST PULP_NNX_PLATFORMS)
-Wno-sign-conversion
-Wno-typedef-redefinition
-Wno-unused-parameter
-Wno-incompatible-pointer-types-discards-qualifiers
-Wno-incompatible-pointer-types
-Wno-discards-qualifiers
)
target_link_libraries(deeploypulp INTERFACE pulp-nnx)
endif()
2 changes: 1 addition & 1 deletion TargetLibraries/third_party/pulp-nn-mixed
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Submodule pulp-nn-mixed updated 74 files
+2 −1 .gitignore
+3 −3 XpulpV2/32bit/src/LinearQuant/pulp_nn_linear_i2_i8_i2.c
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+3 −3 XpulpV2/32bit/src/LinearQuant/pulp_nn_linear_i2_i8_i8.c
+3 −3 XpulpV2/32bit/src/LinearQuant/pulp_nn_linear_i2_u8_i2.c
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+3 −3 XpulpV2/32bit/src/LinearQuant/pulp_nn_linear_i8_u8_i2.c
+3 −3 XpulpV2/32bit/src/LinearQuant/pulp_nn_linear_i8_u8_i4.c
+3 −3 XpulpV2/32bit/src/LinearQuant/pulp_nn_linear_i8_u8_i8.c
+3 −3 XpulpV2/32bit/src/LinearQuant/pulp_nn_linear_u2_i8_i2.c
+3 −3 XpulpV2/32bit/src/LinearQuant/pulp_nn_linear_u2_i8_i4.c
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+3 −3 XpulpV2/32bit/src/LinearQuant/pulp_nn_linear_u2_u8_i2.c
+3 −3 XpulpV2/32bit/src/LinearQuant/pulp_nn_linear_u2_u8_i4.c
+3 −3 XpulpV2/32bit/src/LinearQuant/pulp_nn_linear_u2_u8_i8.c
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+3 −3 XpulpV2/32bit/src/LinearQuant/pulp_nn_linear_u8_i8_i4.c
+3 −3 XpulpV2/32bit/src/LinearQuant/pulp_nn_linear_u8_i8_i8.c
+3 −3 XpulpV2/32bit/src/LinearQuant/pulp_nn_linear_u8_u8_i2.c
+3 −3 XpulpV2/32bit/src/LinearQuant/pulp_nn_linear_u8_u8_i4.c
+3 −3 XpulpV2/32bit/src/LinearQuant/pulp_nn_linear_u8_u8_i8.c
+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_i2_i8_i2.c
+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_i2_i8_i4.c
+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_i2_i8_i8.c
+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_i2_u8_i2.c
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+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_i4_u8_i2.c
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+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_i4_u8_i8.c
+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_i8_i8_i2.c
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+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_i8_u8_i2.c
+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_i8_u8_i4.c
+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_i8_u8_i8.c
+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_u2_i8_i2.c
+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_u2_i8_i4.c
+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_u2_i8_i8.c
+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_u2_u8_i2.c
+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_u2_u8_i4.c
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+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_u4_i8_i2.c
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+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_u4_u8_i2.c
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+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_u8_i8_i2.c
+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_u8_i8_i4.c
+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_u8_i8_i8.c
+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_u8_u8_i2.c
+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_u8_u8_i4.c
+3 −3 XpulpV2/64bit/src/LinearQuant/pulp_nn_linear_u8_u8_i8.c
+3 −3 generators/templates/pulp_nn_linear_q_x_y_z.t
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