publications.md

## Publications

* FPT'20: <a href="https://arxiv.org/pdf/2011.07317.pdf">Memory-Efficient Dataflow Inference for Deep CNNs on FPGA</a>
* IEEE ToC: <a href="https://ieeexplore.ieee.org/abstract/document/9187576/">Evaluation of Optimized CNNs on Heterogeneous Accelerators using a Novel Benchmarking Approach</a>
* FPL'20: <a href="https://arxiv.org/pdf/2004.03021.pdf">LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications</a>
* FCCM'20: <a href="https://www.fccm.org/past/2020/proceedings/2020/pdfs/FCCM2020-65FOvhMqzyMYm99lfeVKyl/580300a238/580300a238.pdf">High-Throughput DNN Inference with LogicNets</a>
* GECCO'20: <a href="https://arxiv.org/pdf/2003.12449.pdf">Evolutionary Bin Packing for Memory-Efficient Dataflow Inference Acceleration on FPGA</a>
* FPGA'20: <a href="https://dl.acm.org/doi/abs/10.1145/3373087.3375348">Evaluation of Optimized CNNs on FPGA and non-FPGA based Accelerators using a Novel Benchmarking Approach</a>
* ACM JETC: <a href="https://arxiv.org/pdf/1909.05009">QuTiBench: Benchmarking neural networks on heterogeneous hardware</a>
* ACM TRETS: <a href="https://arxiv.org/pdf/1901.00370">Optimizing bit-serial matrix multiplication for reconfigurable computing</a>
* FPL'18: <a href="https://arxiv.org/pdf/1807.04093.pdf" target="_blank">FINN-L:Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAs</a>
* FPL'18: <a href="https://arxiv.org/pdf/1806.08862.pdf" target="_blank">BISMO: A Scalable Bit-Serial Matrix Multiplication Overlay for Reconfigurable Computing</a>
* FPL'18: <a href="http://kalman.mee.tcd.ie/fpl2018/content/pdfs/FPL2018-43iDzVTplcpussvbfIaaHz/XZmyRhWvHACdwHRVTCTVB/6jfImwD836ibhOELmms0Ut.pdf" target="_blank">Customizing Low-Precision Deep Neural Networks For FPGAs</a>
* ACM TRETS, Special Issue on Deep Learning: <a href="https://arxiv.org/abs/1809.04570" target="_blank">FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks</a>
* ARC'18: <a href="https://arxiv.org/pdf/1807.10577.pdf" target="_blank">Accuracy to Throughput Trade-Offs for Reduced Precision Neural Networks on Reconfigurable Logic</a>
* CVPR’18: <a href="https://arxiv.org/abs/1807.00301" target="_blank">SYQ: Learning Symmetric Quantization For Efficient Deep Neural Networks</a>
* DATE'18: <a href="https://ieeexplore.ieee.org/abstract/document/8342121/" target="_blank">Inference of quantized neural networks on heterogeneous all-programmable devices</a>
* ICONIP’17: <a href="https://arxiv.org/abs/1709.06262" target="_blank">Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks</a>
* ICCD'17: <a href="https://ieeexplore.ieee.org/abstract/document/8119246/" target="_blank">Scaling Neural Network Performance through Customized Hardware Architectures on Reconfigurable Logic</a>
* PARMA-DITAM'17: <a href="https://arxiv.org/abs/1701.03400" target="_blank">Scaling Binarized Neural Networks on Reconfigurable Logic</a>
* FPGA'17: <a href="https://arxiv.org/abs/1612.07119" target="_blank">FINN: A Framework for Fast, Scalable Binarized Neural Network Inference</a>
* H2RC'16: <a href="https://h2rc.cse.sc.edu/2016/papers/paper_25.pdf" target="_blank">A C++ Library for Rapid Exploration of Binary Neural Networks on Reconfigurable Logic</a>

## External Publications and Projects Based on FINN

If you are using FINN in your
work and would like to be listed here, please contact us!

* <a href="https://coefs.uncc.edu/htabkhiv/teaching/hardware-software-co-design-real-time-ai/" target="_blank">Hardware-Software Co-Design Real-time AI (UNC Charlotte)</a>
* <a href="https://ieeexplore.ieee.org/abstract/document/8442108" target="_blank">BinaryEye: A 20 kfps Streaming Camera System on FPGA with Real-Time On-Device Image Recognition Using Binary Neural Networks</a>
* <a href="https://qiita.com/ykshr/items/08147098516a45203761" target="_blank">Cucumber sorting with FINN (in Japanese)</a>
* <a href="https://github.com/mohaghasemzadeh/ReBNet" target="_blank">ReBNet: Residual Binarized Neural Network, FCCM'18 best paper</a>

## Events, Tutorials and Keynotes
* DAMON'2019 keynote:  <a href="https://github.com/Xilinx/FINN/blob/master/docs/DAMON2019_Blott_final.pdf" target="_blank">Performance Scaling with Innovative Compute Architectures and FPGAs</a>
* Future of AI'2019 keynote:  <a href="https://github.com/Xilinx/FINN/blob/master/docs/FutureofAI2019_Blott.pdf" target="_blank">Future of AI: Unconventional Compute Architectures</a>
* BigData Belfast'2018 talk: <a href="https://github.com/Xilinx/FINN/blob/master/docs/BigDataBelfast2018.pdf" target="_blank">Unconventional Compute Architectures for Enabling the Roll-Out of Deep Learning</a>
* CLUSTER'2018 keynote: <a href="https://github.com/Xilinx/FINN/blob/master/docs/IEEECluster2018.pdf" target="_blank">Unconventional Compute Architectures with Reconfigurable Devices in the Cloud</a>
* RCML'2018 invited talk: <a href="https://github.com/Xilinx/FINN/blob/master/docs/ARC2018.pdf" target="_blank">The Emerging Computational Landscape of Neural Networks</a>
* HotChips'2018 ML tutorial: <a href="https://github.com/Xilinx/FINN/blob/master/docs/Hotchips2018_Tutorial.pdf" target="_blank">Overview of Deep Learning and Computer Architectures for Accelerating DNNs</a>
  + <a href="https://youtu.be/ydsZ7A0FF0I" target="_blank">Video</a>
* ASAP'2018 keynote: <a href="https://github.com/Xilinx/FINN/blob/master/docs/ASAP2018.pdf" target="_blank">Design Trade-offs for Machine Learning Solutions on Reconfigurable Devices</a>
* ARC'2018 keynote: <a href="https://github.com/Xilinx/FINN/blob/master/docs/ARC2018.pdf" target="_blank">Scalable Machine Learning with Reconfigurable Devices</a>
* FPGA'2018 tutorial: <a href="https://github.com/Xilinx/FINN/blob/master/docs/FPGA2018_tutorial.pdf" target="_blank">Training Quantized Neural Networks</a>
* MPSoC 2017 talk: <a href="https://github.com/Xilinx/FINN/blob/master/docs/MPSOC2018.pdf" target="_blank">A Framework for Reduced Precision Neural Networks on FPGAs</a>
* TCD 2017 guest lecture on ML: <a href="https://www.youtube.com/watch?v=pIVh-4tqjPc" target="_blank">Machine Learning for Embedded Systems (Video)</a>
* QPYNQ'2017 tutorial: <a href="https://www.ntnu.edu/ie/eecs/qpynq" target="_blank">Quantized Neural Networks with Xilinx PYNQ</a>