FIFO optimization for qkeras models

[sandeep1404]

Hello all,

My Bram's and LUTs are getting overutilized so I was trying to use the concept of FIFO depth optimization, In that process when I use FIFO depth optimization, I can able to successfully synthesize my 32bit keras model without being quantized, on zcu104 and resolved the issue of overutilization, but when I try to simulate the qkeras 8bit precision model on zcu 104 still the resources are getting overutilized and when I try to use the FIFO depth optimization and then synthesize for the qkeras 8bit fp quantized model, I am facing the following issues:

FileNotFoundError                         Traceback (most recent call last)
Cell In[25], line 84
     71 hls4ml.model.optimizer.get_optimizer('vivadoaccelerator:fifo_depth_optimization').configure(profiling_fifo_depth=100_000) # This will cause even larger use of BRAMs, but is only temporary
     75 # cfg_q = hls4ml.converters.create_config(backend='Vivado')
     76 # cfg_q['IOType']     = 'io_stream' # Must set this if using CNNs!
     77 # cfg_q['HLSConfig']  = hls_config
   (...)
     80 # cfg_q['XilinxPart'] = 'xczu7ev-ffvc1156-2-e'
     81 # hls_model = hls4ml.converters.keras_to_hls(cfg_q)
---> 84 hls_model = hls4ml.converters.convert_from_keras_model(model, 
     85                                                        io_type='io_stream',
     86                                                        hls_config=hls_config,
     87                                                        output_dir='model_zcu104_fpnrnet_11/hls4ml_prj_pynq_fpnrnet_8bit_qkeras_1',
     88 #                                                        board='pynq-z2',
     89                                                       part='xczu7ev-ffvc1156-2-e',
     90                                                       board="zcu104",
     91                                                        backend='VivadoAccelerator'
     92                                                        )

File ~/hls4ml/hls4ml/converters/__init__.py:244, in convert_from_keras_model(model, output_dir, project_name, input_data_tb, output_data_tb, backend, hls_config, **kwargs)
    240 config['HLSConfig']['Model'] = _check_model_config(model_config)
    242 _check_hls_config(config, hls_config)
--> 244 return keras_to_hls(config)

File ~/hls4ml/hls4ml/converters/keras_to_hls.py:395, in keras_to_hls(config)
    393 layer_list, input_layers, output_layers = parse_keras_model(model_arch, reader)
    394 print('Creating HLS model')
--> 395 hls_model = ModelGraph(config, reader, layer_list, input_layers, output_layers)
    396 return hls_model

File ~/hls4ml/hls4ml/model/graph.py:358, in ModelGraph.__init__(self, config, data_reader, layer_list, inputs, outputs)
    355 self._make_graph(layer_list)
    357 for flow in self.config.flows:
--> 358     self.apply_flow(flow)

File ~/hls4ml/hls4ml/model/graph.py:416, in ModelGraph.apply_flow(self, flow, reapply)
    413         return
    415 self._applied_flows.append(applied_flows)
--> 416 self._apply_sub_flow(flow, applied_flows)

File ~/hls4ml/hls4ml/model/graph.py:428, in ModelGraph._apply_sub_flow(self, flow_name, applied_flows)
    425         self._apply_sub_flow(sub_flow, applied_flows)
    427 if len(flow.optimizers) > 0:
--> 428     applied_passes = optimize_model(self, flow.optimizers)
    429 else:
    430     applied_passes = set()

File ~/hls4ml/hls4ml/model/optimizer/optimizer.py:312, in optimize_model(model, passes)
    310 for opt_name, opt in optimizers.items():
    311     if isinstance(opt, ModelOptimizerPass) and opt_name not in applied_passes:
--> 312         res = opt.transform(model)
    313         if res:
    314             applied_passes.add(opt_name)

File ~/hls4ml/hls4ml/backends/vivado_accelerator/passes/fifo_depth_optimization.py:36, in FifoDepthOptimization.transform(self, model)
     33 if profiling_fifo_depth:
     34     set_big_fifos(model.output_vars, profiling_fifo_depth)
---> 36 data = get_vcd_data(model)
     38 for i in range(1, len(data['children'][0]['children'][0]['children'])):
     39     # wrapper fifos
     40     populate_values(
     41         self.values,
     42         data['children'][0]['children'][0]['children'][i]['name'],
     43         data['children'][0]['children'][0]['children'][i]['children'][0]['data'],
     44         data['children'][0]['children'][0]['children'][i]['children'][1]['data'],
     45     )

File ~/hls4ml/hls4ml/backends/vivado/passes/fifo_depth_optimization.py:29, in get_vcd_data(model)
     26 model.write()
     27 model.build(reset=False, csim=True, synth=True, cosim=True, validation=False, export=False, vsynth=False, fifo_opt=True)
---> 29 with open(
     30     model.config.get_output_dir()
     31     + '/'
     32     + model.config.get_project_name()
     33     + '_prj'
     34     + '/solution1/sim/verilog/fifo_opt.vcd'
     35 ) as vcd_file:
     36     vcd = VcdParser()
     37     vcd.parse(vcd_file)

FileNotFoundError: [Errno 2] No such file or directory: 'model_zcu104_fpnrnet_11/hls4ml_prj_pynq_fpnrnet_8bit_qkeras_1/myproject_prj/solution1/sim/verilog/fifo_opt.vcd'

Here is my config file:

import hls4ml




hls_config = hls4ml.utils.config_from_keras_model(model, granularity='name')
# hls_config['Model']['Precision'] = 'ap_fixed<8,1>'
# hls_config["Flows"] = ['output_rounding_saturation_mode']

hls4ml.model.optimizer.get_optimizer('output_rounding_saturation_mode').configure(layers=['Activation'])
hls4ml.model.optimizer.get_optimizer('output_rounding_saturation_mode').configure(rounding_mode = 'AP_RND')
hls4ml.model.optimizer.get_optimizer('output_rounding_saturation_mode').configure(saturation_mode = 'AP_SAT')

############################################

Precision = 'ap_fixed<8,1>'
Reuse_Factor = 1000000

hls_config['Model']['Precision'] = Precision
hls_config['Model']['ReuseFactor'] = Reuse_Factor
hls_config['IOTtype']= 'io_stream'

for Layer in hls_config['LayerName'].keys():
    hls_config['LayerName'][Layer]['Precision'] = Precision
    hls_config['LayerName'][Layer]['Strategy'] = 'resource'
    
hls_config['Model']['Strategy'] = 'Resource'

hls_config['LayerName']['conv0']['ReuseFactor'] = 216
hls_config['LayerName']['conv1']['ReuseFactor'] = 576
hls_config['LayerName']['conv2']['ReuseFactor'] = 576
hls_config['LayerName']['conv3']['ReuseFactor'] = 576
hls_config['LayerName']['conv4']['ReuseFactor'] = 576
# hls_config['LayerName']['conv5']['ReuseFactor'] = 2304
hls_config['LayerName']['conv6']['ReuseFactor'] = 576
hls_config['LayerName']['conv7']['ReuseFactor'] = 64
hls_config['LayerName']['conv8']['ReuseFactor'] = 216


print("-----------------------------------")
print("Configuration")

print(hls_config)
print("-----------------------------------")

hls_config['Flows'] = ['vivadoaccelerator:fifo_depth_optimization']
hls4ml.model.optimizer.get_optimizer('vivadoaccelerator:fifo_depth_optimization').configure(profiling_fifo_depth=100_000) 



hls_model = hls4ml.converters.convert_from_keras_model(model, 
                                                       io_type='io_stream',
                                                       hls_config=hls_config,
                                                       output_dir='model_zcu104_fpnrnet_11/hls4ml_prj_pynq_fpnrnet_8bit_qkeras_fifo',
#                                                        board='pynq-z2',
                                                      part='xczu7ev-ffvc1156-2-e',
                                                      board="zcu104",
                                                       backend='VivadoAccelerator'
                                                       )

please help me in resolving the issue. Thank you in advance.

[sandeep1404]

Hi, can someone look into this issue , when I run the config file, I am getting Filenotfound error, I have no idea where to get that file or something is missing in my code, below are the log file :

Interpreting Model
Topology:
Layer name: input_, layer type: InputLayer, input shapes: [[None, 64, 64, 3]], output shape: [None, 64, 64, 3]
Layer name: conv0, layer type: QConv2D, input shapes: [[None, 64, 64, 3]], output shape: [None, 64, 64, 8]
Layer name: conv1, layer type: QConv2D, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: conv2, layer type: QConv2D, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: conv3, layer type: QConv2D, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: q_activation_3, layer type: Activation, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: add_4, layer type: Merge, input shapes: [[None, 64, 64, 8], [None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: conv4, layer type: QConv2D, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: add_5, layer type: Merge, input shapes: [[None, 64, 64, 8], [None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: q_activation_4, layer type: Activation, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: conv6, layer type: QConv2D, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: conv7, layer type: QConv2D, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: add_6, layer type: Merge, input shapes: [[None, 64, 64, 8], [None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: q_activation_5, layer type: Activation, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: conv8, layer type: QConv2D, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 3]
Layer name: add_7, layer type: Merge, input shapes: [[None, 64, 64, 3], [None, 64, 64, 3]], output shape: [None, 64, 64, 3]
-----------------------------------
Configuration
{'Model': {'Precision': 'ap_fixed<8,1>', 'ReuseFactor': 1000000, 'Strategy': 'Resource', 'BramFactor': 1000000000, 'TraceOutput': False}, 'LayerName': {'input_': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource'}, 'conv0': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource', 'ReuseFactor': 216}, 'conv0_linear': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource'}, 'conv1': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource', 'ReuseFactor': 576}, 'conv1_linear': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource'}, 'conv2': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource', 'ReuseFactor': 576}, 'conv2_linear': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource'}, 'conv3': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource', 'ReuseFactor': 576}, 'conv3_linear': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource'}, 'q_activation_3': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource'}, 'add_4': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource'}, 'conv4': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource', 'ReuseFactor': 576}, 'conv4_linear': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource'}, 'add_5': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource'}, 'q_activation_4': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource'}, 'conv6': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource', 'ReuseFactor': 576}, 'conv6_linear': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource'}, 'conv7': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource', 'ReuseFactor': 64}, 'conv7_linear': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource'}, 'add_6': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource'}, 'q_activation_5': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource'}, 'conv8': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource', 'ReuseFactor': 216}, 'conv8_linear': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource'}, 'add_7': {'Trace': False, 'Precision': 'ap_fixed<8,1>', 'Strategy': 'resource'}}, 'IOTtype': 'io_stream'}
-----------------------------------
Interpreting Model
Topology:
Layer name: input_, layer type: InputLayer, input shapes: [[None, 64, 64, 3]], output shape: [None, 64, 64, 3]
Layer name: conv0, layer type: QConv2D, input shapes: [[None, 64, 64, 3]], output shape: [None, 64, 64, 8]
Layer name: conv1, layer type: QConv2D, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: conv2, layer type: QConv2D, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: conv3, layer type: QConv2D, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: q_activation_3, layer type: Activation, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: add_4, layer type: Merge, input shapes: [[None, 64, 64, 8], [None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: conv4, layer type: QConv2D, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: add_5, layer type: Merge, input shapes: [[None, 64, 64, 8], [None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: q_activation_4, layer type: Activation, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: conv6, layer type: QConv2D, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: conv7, layer type: QConv2D, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: add_6, layer type: Merge, input shapes: [[None, 64, 64, 8], [None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: q_activation_5, layer type: Activation, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 8]
Layer name: conv8, layer type: QConv2D, input shapes: [[None, 64, 64, 8]], output shape: [None, 64, 64, 3]
Layer name: add_7, layer type: Merge, input shapes: [[None, 64, 64, 3], [None, 64, 64, 3]], output shape: [None, 64, 64, 3]
Creating HLS model
WARNING: Config parameter "trace" overwrites an existing attribute in layer "conv7" (PointwiseConv2D)
WARNING: Config parameter "precision" overwrites an existing attribute in layer "conv7" (PointwiseConv2D)
WARNING: Config parameter "strategy" overwrites an existing attribute in layer "conv7" (PointwiseConv2D)
WARNING: Config parameter "reuse_factor" overwrites an existing attribute in layer "conv7" (PointwiseConv2D)
Writing HLS project
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
Done

****** Vivado(TM) HLS - High-Level Synthesis from C, C++ and SystemC v2019.1 (64-bit)
  **** SW Build 2552052 on Fri May 24 14:47:09 MDT 2019
  **** IP Build 2548770 on Fri May 24 18:01:18 MDT 2019
    ** Copyright 1986-2019 Xilinx, Inc. All Rights Reserved.

source /home/sandeep/vivado/Vivado/2019.1/scripts/vivado_hls/hls.tcl -notrace
INFO: [HLS 200-10] Running '/home/sandeep/vivado/Vivado/2019.1/bin/unwrapped/lnx64.o/vivado_hls'
INFO: [HLS 200-10] For user 'sandeep' on host 'sandeep-VirtualBox' (Linux_x86_64 version 5.15.0-71-generic) on Sat Apr 29 17:38:41 IST 2023
INFO: [HLS 200-10] On os Ubuntu 20.04.5 LTS
INFO: [HLS 200-10] In directory '/home/sandeep/hls4ml/model_zcu104_fpnrnet_11/hls4ml_prj_pynq_fpnrnet_8bit_qkeras_fifo'
Sourcing Tcl script 'build_prj.tcl'
INFO: [HLS 200-10] Opening project '/home/sandeep/hls4ml/model_zcu104_fpnrnet_11/hls4ml_prj_pynq_fpnrnet_8bit_qkeras_fifo/myproject_prj'.
INFO: [HLS 200-10] Adding design file 'firmware/myproject_axi.cpp' to the project
INFO: [HLS 200-10] Adding design file 'firmware/myproject.cpp' to the project
INFO: [HLS 200-10] Adding test bench file 'myproject_test.cpp' to the project
INFO: [HLS 200-10] Adding test bench file 'firmware/weights' to the project
INFO: [HLS 200-10] Adding test bench file 'tb_data' to the project
INFO: [HLS 200-10] Opening solution '/home/sandeep/hls4ml/model_zcu104_fpnrnet_11/hls4ml_prj_pynq_fpnrnet_8bit_qkeras_fifo/myproject_prj/solution1'.
INFO: [SYN 201-201] Setting up clock 'default' with a period of 5ns.
INFO: [SYN 201-201] Setting up clock 'default' with an uncertainty of 0.625ns.
INFO: [HLS 200-10] Setting target device to 'xczu7ev-ffvc1156-2-e'
INFO: [XFORM 203-101] Allowed max sub elements number after partition is 4096.
INFO: [XFORM 203-1161] The maximum of name length is set into 80.
INFO: [XFORM 203-101] Allowed max sub elements number after partition is 4096.
INFO: [XFORM 203-1161] The maximum of name length is set into 80.
***** C SIMULATION *****
INFO: [SIM 211-2] *************** CSIM start ***************
INFO: [SIM 211-4] CSIM will launch GCC as the compiler.
   Compiling ../../../../myproject_test.cpp in debug mode
../../../../myproject_test.cpp: In function ‘int main(int, char**)’:
../../../../myproject_test.cpp:80:34: error: ‘inputst’ was not declared in this scope
       nnet::copy_data_axi<float, inputst, 0, N_IN>(in, inputs);
                                  ^~~~~~~
../../../../myproject_test.cpp:80:62: error: no matching function for call to ‘copy_data_axi(std::vector<float>&, input_axi_t [12288])’
       nnet::copy_data_axi<float, inputst, 0, N_IN>(in, inputs);
                                                              ^
In file included from ../../../../myproject_test.cpp:29:0:
../../../../firmware/nnet_utils/nnet_helpers.h:274:70: note: candidate: template<class src_T, class dst_T, long unsigned int OFFSET, long unsigned int SIZE> void nnet::copy_data_axi(std::vector<_Tp>, dst_T*)
 template <class src_T, class dst_T, size_t OFFSET, size_t SIZE> void copy_data_axi(std::vector<src_T> src, dst_T dst[SIZE]) {
                                                                      ^~~~~~~~~~~~~
../../../../firmware/nnet_utils/nnet_helpers.h:274:70: note:   template argument deduction/substitution failed:
../../../../myproject_test.cpp:80:62: error: template argument 2 is invalid
       nnet::copy_data_axi<float, inputst, 0, N_IN>(in, inputs);
                                                              ^
make: *** [csim.mk:76: obj/myproject_test.o] Error 1
ERROR: [SIM 211-100] 'csim_design' failed: compilation error(s).
INFO: [SIM 211-3] *************** CSIM finish ***************
4
    while executing
"source build_prj.tcl"
    ("uplevel" body line 1)
    invoked from within
"uplevel \#0 [list source $arg] "

INFO: [Common 17-206] Exiting vivado_hls at Sat Apr 29 17:38:43 2023...
CSynthesis report not found.
Vivado synthesis report not found.
Cosim report not found.
Timing report not found.
CSynthesis report not found.
Vivado synthesis report not found.
Cosim report not found.
Timing report not found.
---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
Cell In[27], line 84
     71 hls4ml.model.optimizer.get_optimizer('vivado:fifo_depth_optimization').configure(profiling_fifo_depth=100_000) # This will cause even larger use of BRAMs, but is only temporary
     75 # cfg_q = hls4ml.converters.create_config(backend='Vivado')
     76 # cfg_q['IOType']     = 'io_stream' # Must set this if using CNNs!
     77 # cfg_q['HLSConfig']  = hls_config
   (...)
     80 # cfg_q['XilinxPart'] = 'xczu7ev-ffvc1156-2-e'
     81 # hls_model = hls4ml.converters.keras_to_hls(cfg_q)
---> 84 hls_model = hls4ml.converters.convert_from_keras_model(model, 
     85                                                        io_type='io_stream',
     86                                                        hls_config=hls_config,
     87                                                        output_dir='model_zcu104_fpnrnet_11/hls4ml_prj_pynq_fpnrnet_8bit_qkeras_fifo',
     88 #                                                        board='pynq-z2',
     89                                                       part='xczu7ev-ffvc1156-2-e',
     90                                                       board="zcu104",
     91                                                        backend='VivadoAccelerator'
     92                                                        )

File ~/hls4ml/hls4ml/converters/__init__.py:244, in convert_from_keras_model(model, output_dir, project_name, input_data_tb, output_data_tb, backend, hls_config, **kwargs)
    240 config['HLSConfig']['Model'] = _check_model_config(model_config)
    242 _check_hls_config(config, hls_config)
--> 244 return keras_to_hls(config)

File ~/hls4ml/hls4ml/converters/keras_to_hls.py:395, in keras_to_hls(config)
    393 layer_list, input_layers, output_layers = parse_keras_model(model_arch, reader)
    394 print('Creating HLS model')
--> 395 hls_model = ModelGraph(config, reader, layer_list, input_layers, output_layers)
    396 return hls_model

File ~/hls4ml/hls4ml/model/graph.py:358, in ModelGraph.__init__(self, config, data_reader, layer_list, inputs, outputs)
    355 self._make_graph(layer_list)
    357 for flow in self.config.flows:
--> 358     self.apply_flow(flow)

File ~/hls4ml/hls4ml/model/graph.py:416, in ModelGraph.apply_flow(self, flow, reapply)
    413         return
    415 self._applied_flows.append(applied_flows)
--> 416 self._apply_sub_flow(flow, applied_flows)

File ~/hls4ml/hls4ml/model/graph.py:428, in ModelGraph._apply_sub_flow(self, flow_name, applied_flows)
    425         self._apply_sub_flow(sub_flow, applied_flows)
    427 if len(flow.optimizers) > 0:
--> 428     applied_passes = optimize_model(self, flow.optimizers)
    429 else:
    430     applied_passes = set()

File ~/hls4ml/hls4ml/model/optimizer/optimizer.py:312, in optimize_model(model, passes)
    310 for opt_name, opt in optimizers.items():
    311     if isinstance(opt, ModelOptimizerPass) and opt_name not in applied_passes:
--> 312         res = opt.transform(model)
    313         if res:
    314             applied_passes.add(opt_name)

File ~/hls4ml/hls4ml/backends/vivado_accelerator/passes/fifo_depth_optimization.py:36, in FifoDepthOptimization.transform(self, model)
     33 if profiling_fifo_depth:
     34     set_big_fifos(model.output_vars, profiling_fifo_depth)
---> 36 data = get_vcd_data(model)
     38 for i in range(1, len(data['children'][0]['children'][0]['children'])):
     39     # wrapper fifos
     40     populate_values(
     41         self.values,
     42         data['children'][0]['children'][0]['children'][i]['name'],
     43         data['children'][0]['children'][0]['children'][i]['children'][0]['data'],
     44         data['children'][0]['children'][0]['children'][i]['children'][1]['data'],
     45     )

File ~/hls4ml/hls4ml/backends/vivado/passes/fifo_depth_optimization.py:29, in get_vcd_data(model)
     26 model.write()
     27 model.build(reset=False, csim=True, synth=True, cosim=True, validation=False, export=False, vsynth=False, fifo_opt=True)
---> 29 with open(
     30     model.config.get_output_dir()
     31     + '/'
     32     + model.config.get_project_name()
     33     + '_prj'
     34     + '/solution1/sim/verilog/fifo_opt.vcd'
     35 ) as vcd_file:
     36     vcd = VcdParser()
     37     vcd.parse(vcd_file)

FileNotFoundError: [Errno 2] No such file or directory: 'model_zcu104_fpnrnet_11/hls4ml_prj_pynq_fpnrnet_8bit_qkeras_fifo/myproject_prj/solution1/sim/verilog/fifo_opt.vcd'