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ORCIDMichaela Blott
- > Home > Persons > Michaela Blott
Publications
- 2025
[i31]Giuseppe Franco, Pablo Monteagudo-Lago, Ian Colbert, Nicholas J. Fraser, Michaela Blott:
Improving Quantization with Post-Training Model Expansion. CoRR abs/2503.17513 (2025)- 2023
- [j15]Timoteo García Bertoa
, Giulio Gambardella, Nicholas J. Fraser, Michaela Blott, John McAllister:
Fault-Tolerant Neural Network Accelerators With Selective TMR. IEEE Des. Test 40(2): 67-74 (2023) - 2021
- [j6]Michaela Blott, Nicholas J. Fraser, Giulio Gambardella, Lisa Halder, Johannes Kath, Zachary Neveu, Yaman Umuroglu, Alina Vasilciuc, Miriam Leeser, Linda Doyle:
Evaluation of Optimized CNNs on Heterogeneous Accelerators Using a Novel Benchmarking Approach. IEEE Trans. Computers 70(10): 1654-1669 (2021) - 2020
- [c36]Yaman Umuroglu, Yash Akhauri, Nicholas J. Fraser, Michaela Blott:
High-Throughput DNN Inference with LogicNets. FCCM 2020: 238 - [c34]Michaela Blott, Johannes Kath, Lisa Halder, Yaman Umuroglu, Nicholas J. Fraser, Giulio Gambardella, Miriam Leeser, Linda Doyle:
Evaluation of Optimized CNNs on FPGA and non-FPGA based Accelerators using a Novel Benchmarking Approach. FPGA 2020: 317 - [c33]Yaman Umuroglu, Yash Akhauri, Nicholas James Fraser, Michaela Blott:
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications. FPL 2020: 291-297 - [c30]Lucian Petrica, Tobias Alonso, Mairin Kroes, Nicholas J. Fraser, Sorin Cotofana, Michaela Blott:
Memory-Efficient Dataflow Inference for Deep CNNs on FPGA. FPT 2020: 48-55 - [c29]Ussama Zahid, Giulio Gambardella, Nicholas J. Fraser, Michaela Blott, Kees A. Vissers:
FAT: Training Neural Networks for Reliable Inference Under Hardware Faults. ITC 2020: 1-10 - [i17]Yaman Umuroglu, Yash Akhauri, Nicholas J. Fraser, Michaela Blott:
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications. CoRR abs/2004.03021 (2020) - [i16]Ussama Zahid, Giulio Gambardella, Nicholas J. Fraser, Michaela Blott, Kees A. Vissers:
FAT: Training Neural Networks for Reliable Inference Under Hardware Faults. CoRR abs/2011.05873 (2020) - [i15]Lucian Petrica, Tobias Alonso, Mairin Kroes, Nicholas J. Fraser, Sorin Cotofana, Michaela Blott:
Memory-Efficient Dataflow Inference for Deep CNNs on FPGA. CoRR abs/2011.07317 (2020) - 2018
- [j4]Michaela Blott, Thomas B. Preußer, Nicholas J. Fraser, Giulio Gambardella, Kenneth O'Brien, Yaman Umuroglu, Miriam Leeser, Kees A. Vissers:
FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks. ACM Trans. Reconfigurable Technol. Syst. 11(3): 16:1-16:23 (2018) - [c26]Jiang Su, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Gianluca Durelli, David B. Thomas, Philip Heng Wai Leong, Peter Y. K. Cheung:
Accuracy to Throughput Trade-Offs for Reduced Precision Neural Networks on Reconfigurable Logic. ARC 2018: 29-42 - [c25]Julian Faraone, Nicholas J. Fraser, Michaela Blott, Philip H. W. Leong:
SYQ: Learning Symmetric Quantization for Efficient Deep Neural Networks. CVPR 2018: 4300-4309 - [c24]Thomas B. Preußer, Giulio Gambardella, Nicholas J. Fraser, Michaela Blott:
Inference of quantized neural networks on heterogeneous all-programmable devices. DATE 2018: 833-838 - [c22]Julian Faraone, Giulio Gambardella, Nicholas J. Fraser, Michaela Blott, Philip H. W. Leong, David Boland:
Customizing Low-Precision Deep Neural Networks for FPGAs. FPL 2018: 97-100 - [i12]Thomas B. Preußer, Giulio Gambardella, Nicholas J. Fraser, Michaela Blott:
Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices. CoRR abs/1806.08085 (2018) - [i11]Julian Faraone, Nicholas J. Fraser, Michaela Blott, Philip Heng Wai Leong:
SYQ: Learning Symmetric Quantization For Efficient Deep Neural Networks. CoRR abs/1807.00301 (2018) - [i10]Michaela Blott, Thomas B. Preußer, Nicholas J. Fraser, Giulio Gambardella, Kenneth O'Brien, Yaman Umuroglu, Miriam Leeser:
Scaling Neural Network Performance through Customized Hardware Architectures on Reconfigurable Logic. CoRR abs/1807.03123 (2018) - [i8]Jiang Su, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Gianluca Durelli, David B. Thomas, Philip Heng Wai Leong, Peter Y. K. Cheung:
Accuracy to Throughput Trade-offs for Reduced Precision Neural Networks on Reconfigurable Logic. CoRR abs/1807.10577 (2018) - [i7]Michaela Blott, Thomas B. Preußer, Nicholas J. Fraser, Giulio Gambardella, Kenneth O'Brien, Yaman Umuroglu:
FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks. CoRR abs/1809.04570 (2018) - 2017
- [c18]Yaman Umuroglu, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Philip Heng Wai Leong, Magnus Jahre, Kees A. Vissers:
FINN: A Framework for Fast, Scalable Binarized Neural Network Inference. FPGA 2017: 65-74 - [c17]Nicholas J. Fraser, Yaman Umuroglu, Giulio Gambardella, Michaela Blott, Philip Heng Wai Leong, Magnus Jahre, Kees A. Vissers:
Scaling Binarized Neural Networks on Reconfigurable Logic. PARMA-DITAM@HiPEAC 2017: 25-30 - [c16]Michaela Blott, Thomas B. Preußer, Nicholas J. Fraser, Giulio Gambardella, Kenneth O'Brien, Yaman Umuroglu, Miriam Leeser:
Scaling Neural Network Performance through Customized Hardware Architectures on Reconfigurable Logic. ICCD 2017: 419-422 - [c15]Julian Faraone, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Philip Heng Wai Leong:
Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks. ICONIP (2) 2017: 393-404 - [i5]Nicholas J. Fraser, Yaman Umuroglu, Giulio Gambardella, Michaela Blott, Philip Heng Wai Leong, Magnus Jahre, Kees A. Vissers:
Scaling Binarized Neural Networks on Reconfigurable Logic. CoRR abs/1701.03400 (2017) - [i4]Julian Faraone, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Philip Heng Wai Leong:
Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks. CoRR abs/1709.06262 (2017) - 2016
- [i2]Yaman Umuroglu, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Philip Heng Wai Leong, Magnus Jahre, Kees A. Vissers:
FINN: A Framework for Fast, Scalable Binarized Neural Network Inference. CoRR abs/1612.07119 (2016)
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