* [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

* [Build] add a deployment_package step

* [Build] typo fix in build examples

* [Build] fix deployment_package step

* [Analysis] ensure python integers in fpgadataflow analysis passes

* [Build] report generation and other minor improvements

* [Docs] update build_dataflow docs

* [Build] latency est. fix

* [Build] check if fold config is None

* [Build] add ooc synthesis step

* [Deps] update finn-base

* [Util] out_of_context_synth: remove remote, use launch_process_helper

* [Build] include all outputs in examples configs

* [Docs] update build flow docs

* [Deps] update finn-base

* [Util] bugfix in launch_process_helper call

* [Docker] use interactive mode for builds

* [Build] enable pdb debugging for builds

* [Refactor] move build functions to own submodule

* [Test] build_dataflow: fix expected files

* [Build] report estimated resource total

* [Infra] remove old eggs

* [HLSCustomOp] introduce get_op_counts

only implemented for MVAU and VVAU for now

* [HLSCustomOp] extend get_op_counts to include params too

* [Analysis] introduce op_and_param_counts pass

* [Build] generate op/param counts as part of estimates + add doc

* [HLSCustomOp] assert if ap_int_max_w is too large

* [StreamingFC] fix ap_int_max_w calculation

* [Build] minor fix in step_generate_estimate_reports

* [StreamingFC] enable decoupled URAM weights

* [StreamingFC export 0-valued .dat for decoupled uram

* [Zynq] bugfix: AXI MM and lite IF counts were switched around

* [Zynq] support wiring up multiple AXI lites in shell

* [Deps] update finn-base

* [HLSCustomOp] introduce uram_efficiency_estimation

* [StreamingFC] implement uram eff est

* [FIFO] fix ip packaging problems

* [Thres] better integer check for thresholds

* [HLSCustomOp] rework infer_node_datatype to be more flexible

allow re-setting of inputDataType if it changed during datatype
inference

* [Thres] bugfix in integer thres check

* [Thres] bugfix in integer thres check

* [Docker] relax instance name, only fwd ports in notebook mode

* [Driver] generate runtime weight files for appropriate layers

* [Driver] draft a first version of load_runtime_weights

* [Driver] fixes&enhancements to load_runtime_weights

* [Driver] typo fix in split

* [Test] use runtime weights for tfc end2end

* [Build] bugfix in ooc step

* [Driver] also handle runtime-writable thresholds

* [Thresholding] implement get_op_and_param_counts

* [Test] use tfc-w1a1 as standalone thresholds end2end testcase

* [Build] add option for standalone thresholds

* [Driver] update comments

* [Driver] overhaul driver, split up template

* [Test] fix test_res_estimate expectation

* [Driver] fix varname in template

* [Build] add a deployment_package step

* [Build] typo fix in build examples

* [Build] fix deployment_package step

* [Analysis] ensure python integers in fpgadataflow analysis passes

* [Build] report generation and other minor improvements

* [Docs] update build_dataflow docs

* [Build] latency est. fix

* [Build] check if fold config is None

* [Build] add ooc synthesis step

* [Deps] update finn-base

* [Util] out_of_context_synth: remove remote, use launch_process_helper

* [Build] include all outputs in examples configs

* [Docs] update build flow docs

* [Deps] update finn-base

* [Util] bugfix in launch_process_helper call

* [Docker] use interactive mode for builds

* [Build] enable pdb debugging for builds

* [Refactor] move build functions to own submodule

* [Test] build_dataflow: fix expected files

* [Build] report estimated resource total

* [Infra] remove old eggs

* [HLSCustomOp] introduce get_op_counts

only implemented for MVAU and VVAU for now

* [HLSCustomOp] extend get_op_counts to include params too

* [Analysis] introduce op_and_param_counts pass

* [Build] generate op/param counts as part of estimates + add doc

* [HLSCustomOp] assert if ap_int_max_w is too large

* [StreamingFC] fix ap_int_max_w calculation

* [Build] minor fix in step_generate_estimate_reports

Set vivado DWC input width explicitly as by default a block will have a 1 byte input which probably will cause a bottleneck (#254)