publications.md



Publications

FPL'18: FINN-L:Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAs

FPL'18: BISMO: A Scalable Bit-Serial Matrix Multiplication Overlay for Reconfigurable Computing

FPL'18: Customizing Low-Precision Deep Neural Networks For FPGAs

ACM TRETS, Special Issue on Deep Learning: FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks

ARC'18: Accuracy to Throughput Trade-Offs for Reduced Precision Neural Networks on Reconfigurable Logic

CVPR’18: SYQ: Learning Symmetric Quantization For Efﬁcient Deep Neural Networks

DATE'18: Inference of quantized neural networks on heterogeneous all-programmable devices

ICONIP’17: Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks

ICCD'17: Scaling Neural Network Performance through Customized Hardware Architectures on Reconfigurable Logic

PARMA-DITAM'17: Scaling Binarized Neural Networks on Reconfigurable Logic

FPGA'17: FINN: A Framework for Fast, Scalable Binarized Neural Network Inference

H2RC'16: A C++ Library for Rapid Exploration of Binary Neural Networks on Reconfigurable Logic


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!

Hardware-Software Co-Design Real-time AI (UNC Charlotte)
BinaryEye: A 20 kfps Streaming Camera System on FPGA with Real-Time On-Device Image Recognition Using Binary Neural Networks
Cucumber sorting with FINN (in Japanese)
ReBNet: Residual Binarized Neural Network, FCCM'18 best paper


Events, Tutorials and Keynotes

DAMON'2019 keynote:  Performance Scaling with Innovative Compute Architectures and FPGAs

Future of AI'2019 keynote:  Future of AI: Unconventional Compute Architectures

BigData Belfast'2018 talk: Unconventional Compute Architectures for Enabling the Roll-Out of Deep Learning

CLUSTER'2018 keynote: Unconventional Compute Architectures with Reconfigurable Devices in the Cloud

RCML'2018 invited talk: The Emerging Computational Landscape of Neural Networks

HotChips'2018 ML tutorial: Overview of Deep Learning and Computer Architectures for Accelerating DNNs

Video


ASAP'2018 keynote: Design Trade-offs for Machine Learning Solutions on Reconfigurable Devices

ARC'2018 keynote: Scalable Machine Learning with Reconfigurable Devices

FPGA'2018 tutorial: Training Quantized Neural Networks

MPSoC 2017 talk: A Framework for Reduced Precision Neural Networks on FPGAs

TCD 2017 guest lecture on ML: Machine Learning for Embedded Systems (Video)

QPYNQ'2017 tutorial: Quantized Neural Networks with Xilinx PYNQ