Adjust notebook to changes in bnn-pynq brevitas example

[test_move_identical_op_past_join_op]: added test case for new transformation.

* Assign BD address segments based on actual range

* Change minimum address range to 4k

* [Test] lfc end2end tests use runtime-wr weights instead of tfc

Co-authored-by: Yaman Umuroglu <yamanu@xilinx.com>

* remove obsolete netron params

* set default ip and port

* set host and port as fixed params

* consistency

* Add MoveMulPastMaxPool transformation to streamlining (#285)

* Add MoveMulPastMaxPool transform to streamlining

* Add second call to transformation within streamlining

* [Docker] set Netron version to >=4.7.9

Co-authored-by: Neil Kim Nielsen <37108154+neilnielsen@users.noreply.github.com>
Co-authored-by: Felix Jentzsch <45395194+fpjentzsch@users.noreply.github.com>
Co-authored-by: Yaman Umuroglu <yamanu@xilinx.com>

* Add MoveMulPastMaxPool transform to streamlining

* Add second call to transformation within streamlining

* [Notebook] start custom op nb

* [Notebook] update custom_op notebook

* [Notebook] update the custom op notebook to reflect new reg system

* [Notebook] Added more descriptive text into the notebook (#278)

* [Web] update publications

* Build step: measure rtlsim performance (#262)

* [Build] introduce step_measure_rtlsim_performance

* [Docs] document the rtlsim performance step

* [Docs] add comments on CPU/RAM/storage recommandations

* [Build, Test] enable rtlsim perf as part of build test too

* [Build] print where intermediate outputs are generated

* [Docs] remove ghpages content, add note

* [Docker] use -c continue for build_custom mode

* [Docs] minor fixes

* Driver data packing + improvements (#261)

* [Driver] add driver_base.py as own template file + comments

* [Driver] also move validation to won template + use in transform

* [Driver] more comments

* [Driver] suggested updates from PYNQ team + async mode exec_on_buffers

* [Driver] allow smaller batchsize in execute_on_buffers

* [Driver] optimize buffer alloc a bit

* [Driver] wait condition fix

* [Deps] update finn-base

* [Deps] update finn-base

* [Driver] enable fast_mode, expose more benchmarks

* [Deps] update finn-base

* [Driver] handle case with no rt weights

* [Build] auto-exit build_custom if no errors

* [Driver] get Alveo clock during test

* Add report_utilization to stitched project tcl script (#243)

* [Vitis] use -mode batch for report gen Vivado launch

* Feature/cybersecurity notebook (#259)

* Created cybersecurity notebook and downloaded data with wget

* Reorganized the cybersecurity notebook

* Read the entire dataset as a pytorch tensor and trained a simple MLP on the UNSW_NB15 dataset

* small changes to markdown

* Training and testing have the same integer encoding

* Added debugger tool

* added loss visualization

* aded 1hot encoder and separated dataloader

Added 1-hot encoder with scikit learn.
Seperated the dataloader into a python file.

* changes to get 99.998% accuracy

* changed loss plot

* accuracy at 70% after 50 epochs

However, loss is not ok

* got 75% accuracy

* see loss

* added scheduled statistics

* added iterator over all possible parameters

* updates on automation

debugging model error

* Delete cybersecurity-checkpoint.ipynb

* Delete cybersecurity_2-checkpoint.ipynb

* Delete exemplofixe-checkpoint.ipynb

* Delete UNSW_NB15_testing-set.csv

* Delete UNSW_NB15_train.csv

* Delete UNSW_NB15_training-set.csv

* Delete UNSW_NB15_val.csv

* general cleanup

* general cleanup

* updates with 75.7238% accuracy

* added quantization of the dataset

* debugging the quantization of the dataset

* added plots for the debugging

* debugging the quantization. Show the differences

* update debugging

* updates on debugging

* updates on debugging quantization

* updates on debug, uint32 df added

* updates on debugging

* added 2 pictures for the debugging

* Added quantization of the dataset

* aded results for training the model with the quantized dataset

* added quantization of the dataset

* added new notebook with model definition with brevitas

* cleaning up documents

* modified the model definition

* Changed the loss function

* Added debug with pdb

* Successfully created neural network with Brevitas

* Correctly quantize the dataset

* Added export of onnx model

* Added FINN validation of the Brevitas model

* improved dataloader

* general cleanup

* General Cleanup

* General Cleanup

* Completed verifying the FINN model against Brevitas

* Added new layer to MLP - Debugging

* verified that the MLP model with new layer (QuantIdentity) outputs the same in Brevitas and in FINN for all 82332 test inputs

* verified that the MLP model with new layer (QuantIdentity) outputs the same in Brevitas and in FINN for all 82332 test inputs

* verified that model with new layer (QuantIdentity) outputs the same in Brevitas and in FINN for all 82332 inputs

* verified that model with new layer and input shifted to accept {-1,1}, outputs the same in Brevitas (input is in {-1,+1}) and in FINN (input is in {-1,+1})  for the 82332 inputs

* General cleanup and added text

* General cleanup: improved text

* General cleanup: fixed text typos

* General cleanup: Added text

* Delete cybersecurity.ipynb

* Delete dataloader.py

* Rename cybersecurity_Brevitas_1bit.ipynb to 1-cybersecurity-Brevitas-1bit.ipynb

* Rename cybersecurity_Brevitas_Verification.ipynb to 2-cybersecurity-finn-verification.ipynb

* Added the last notebook

* Added last notebook describing the finn build

* Added changed parameters to see differences

* Added changed parameters and added text

* Fixed typo

* [Notebooks] reorganize into folders, add README for cybsec

* [Notebook] add license header and refs to cybsec dataset quantizer

* [Notebooks] rename cybsec notebook files

* [Notebook] first pass thru cybsec part 1

* [Notebook] refactor more of cybsec part 1

* [Notebook] add option to use pretrained weights

* fixed outline and typo

* [Notebook] update cybsec notebook #2 and gitignore

* [Notebook] start refactoring cybsec part 3

* [ConvertToHLS] allow out_scale=2 for bipolar MT

* [Build] add alternative set of steps for estimation only

* [Transform] attempt to handle padding for IODMAs

* [Transform] explicitly ignore IODMA nodes for InsertDWC

* [Notebook] full pass over cybsec notebook 3

* [Util] move vivado utils into finn-base

* fixed outline

* [HLSCustomOp] better err msg on ipgen failure

Co-authored-by: Alina Vasilciuc <alinav@xlnx.xilinx.com>
Co-authored-by: Yaman Umuroglu <yamanu@xilinx.com>

* [Notebook] add README, remove mobilenet notebook

* [Docs] round of updates

* [Docs] make stack image local

* [Docs] apidocs updates

* [Docs] update README for v0.5b

* [Release] merge dev into master for v0.5b

* [Docs] bring back missing img

* [Docs] bring back missing images

* Switch hlslib comparison functions (#263)

* [Test] add lfc to end2end tests

* [Deps] update hlslib to get comp:: fxns

* [HLS] use comp:: comparators instead of std::

* [VVAU] hlslib now uses inner prod dim instead of K

* [Thres] manually workaround vivado_hls bug for T[0][0]=0

* [Infra] add .vscode to .gitignore

* [Build] separate HLS codegen and ipgen steps (#265)

* [Docs] first version of developer docs

* [Notebook] fix broken resource paths

* Fix Im2Col attributes for newer finn-base (#276)

* [Deps] update finn-base

* [Refactor] fix im2col props for updated finn-base

* [Docs ] Add FAQ page to the Documentation (#273)

* [Docs] Add FAQ page into the Docs

* [Docs] some minor text format changes.

* [Docs] updates to FAQ

Co-authored-by: Yaman Umuroglu <yaman.umuroglu@xilinx.com>

* [Notebook] Added more descriptive text into the notebook

* Fix ZCU102 support in Vivado shell project (#280)

* [ConvertToHLS] fix conversion for bipolar outputs

* [Notebook] updates to custom op notebook

Co-authored-by: Yaman Umuroglu <yamanu@xilinx.com>
Co-authored-by: Tobi-Alonso <tobi.alonso@gmail.com>
Co-authored-by: alinavalinav <60705229+alinavalinav@users.noreply.github.com>
Co-authored-by: Alina Vasilciuc <alinav@xlnx.xilinx.com>
Co-authored-by: Yaman Umuroglu <yaman.umuroglu@xilinx.com>
Co-authored-by: Felix Jentzsch <45395194+fpjentzsch@users.noreply.github.com>

Co-authored-by: jalezeta <51440887+jalezeta@users.noreply.github.com>
Co-authored-by: Tobi-Alonso <tobi.alonso@gmail.com>
Co-authored-by: alinavalinav <60705229+alinavalinav@users.noreply.github.com>
Co-authored-by: Alina Vasilciuc <alinav@xlnx.xilinx.com>
Co-authored-by: Felix Jentzsch <45395194+fpjentzsch@users.noreply.github.com>

* [Docs] Add FAQ page into the Docs

* [Docs] some minor text format changes.

* [Docs] updates to FAQ

Co-authored-by: Yaman Umuroglu <yaman.umuroglu@xilinx.com>

* [Deps] update finn-base

* [Refactor] fix im2col props for updated finn-base

* [Test] add lfc to end2end tests

* [Deps] update hlslib to get comp:: fxns

* [HLS] use comp:: comparators instead of std::

* [VVAU] hlslib now uses inner prod dim instead of K

* [Thres] manually workaround vivado_hls bug for T[0][0]=0

* Created cybersecurity notebook and downloaded data with wget

* Reorganized the cybersecurity notebook

* Read the entire dataset as a pytorch tensor and trained a simple MLP on the UNSW_NB15 dataset

* small changes to markdown

* Training and testing have the same integer encoding

* Added debugger tool

* added loss visualization

* aded 1hot encoder and separated dataloader

Added 1-hot encoder with scikit learn.
Seperated the dataloader into a python file.

* changes to get 99.998% accuracy

* changed loss plot

* accuracy at 70% after 50 epochs

However, loss is not ok

* got 75% accuracy

* see loss

* added scheduled statistics

* added iterator over all possible parameters

* updates on automation

debugging model error

* Delete cybersecurity-checkpoint.ipynb

* Delete cybersecurity_2-checkpoint.ipynb

* Delete exemplofixe-checkpoint.ipynb

* Delete UNSW_NB15_testing-set.csv

* Delete UNSW_NB15_train.csv

* Delete UNSW_NB15_training-set.csv

* Delete UNSW_NB15_val.csv

* general cleanup

* general cleanup

* updates with 75.7238% accuracy

* added quantization of the dataset

* debugging the quantization of the dataset

* added plots for the debugging

* debugging the quantization. Show the differences

* update debugging

* updates on debugging

* updates on debugging quantization

* updates on debug, uint32 df added

* updates on debugging

* added 2 pictures for the debugging

* Added quantization of the dataset

* aded results for training the model with the quantized dataset

* added quantization of the dataset

* added new notebook with model definition with brevitas

* cleaning up documents

* modified the model definition

* Changed the loss function

* Added debug with pdb

* Successfully created neural network with Brevitas

* Correctly quantize the dataset

* Added export of onnx model

* Added FINN validation of the Brevitas model

* improved dataloader

* general cleanup

* General Cleanup

* General Cleanup

* Completed verifying the FINN model against Brevitas

* Added new layer to MLP - Debugging

* verified that the MLP model with new layer (QuantIdentity) outputs the same in Brevitas and in FINN for all 82332 test inputs

* verified that the MLP model with new layer (QuantIdentity) outputs the same in Brevitas and in FINN for all 82332 test inputs

* verified that model with new layer (QuantIdentity) outputs the same in Brevitas and in FINN for all 82332 inputs

* verified that model with new layer and input shifted to accept {-1,1}, outputs the same in Brevitas (input is in {-1,+1}) and in FINN (input is in {-1,+1})  for the 82332 inputs

* General cleanup and added text

* General cleanup: improved text

* General cleanup: fixed text typos

* General cleanup: Added text

* Delete cybersecurity.ipynb

* Delete dataloader.py

* Rename cybersecurity_Brevitas_1bit.ipynb to 1-cybersecurity-Brevitas-1bit.ipynb

* Rename cybersecurity_Brevitas_Verification.ipynb to 2-cybersecurity-finn-verification.ipynb

* Added the last notebook

* Added last notebook describing the finn build

* Added changed parameters to see differences

* Added changed parameters and added text

* Fixed typo

* [Notebooks] reorganize into folders, add README for cybsec

* [Notebook] add license header and refs to cybsec dataset quantizer

* [Notebooks] rename cybsec notebook files

* [Notebook] first pass thru cybsec part 1

* [Notebook] refactor more of cybsec part 1

* [Notebook] add option to use pretrained weights

* fixed outline and typo

* [Notebook] update cybsec notebook #2 and gitignore

* [Notebook] start refactoring cybsec part 3

* [ConvertToHLS] allow out_scale=2 for bipolar MT

* [Build] add alternative set of steps for estimation only

* [Transform] attempt to handle padding for IODMAs

* [Transform] explicitly ignore IODMA nodes for InsertDWC

* [Notebook] full pass over cybsec notebook 3

* [Util] move vivado utils into finn-base

* fixed outline

* [HLSCustomOp] better err msg on ipgen failure

Co-authored-by: Alina Vasilciuc <alinav@xlnx.xilinx.com>
Co-authored-by: Yaman Umuroglu <yamanu@xilinx.com>

* [Driver] add driver_base.py as own template file + comments

* [Driver] also move validation to won template + use in transform

* [Driver] more comments

* [Driver] suggested updates from PYNQ team + async mode exec_on_buffers

* [Driver] allow smaller batchsize in execute_on_buffers

* [Driver] optimize buffer alloc a bit

* [Driver] wait condition fix

* [Deps] update finn-base

* [Deps] update finn-base

* [Driver] enable fast_mode, expose more benchmarks

* [Deps] update finn-base

* [Build] introduce step_measure_rtlsim_performance

* [Docs] document the rtlsim performance step

* [Docs] add comments on CPU/RAM/storage recommandations

* [Build, Test] enable rtlsim perf as part of build test too

* [Build] print where intermediate outputs are generated

* [Docs] remove ghpages content, add note

* [Docker] use -c continue for build_custom mode

* [Docs] minor fixes

* [Build] add verification-related options to DataflowBuildConfig

* [Build] add verification to build steps

* [Test] build_dataflow: check for verification outputs

* [Docs] add documentation on build verification steps