After obtaining master degree in computer science and technology from PLA Science and Technology University in Nanjing, Jiangsu Province in June 2017, I worked in National Internet Emergency Center (CNCERT/CC) Shanghai Branch from July 2017 to July 2019. Currently, I am chasing my Ph.D. degree in CS. in Shanghai Jiao Tong University Advanced Computer Architecture Laboratory (ACA Lab), supervised by Prof. Li Jiang, focusing on the research of efficient AI/ML algorithms, in-memory computing, software-hardware co-design, and neuromorphic computing. I am also selected into Wenjun Wu Honored Ph.D Class.

The papers and related resources will be shared on my Github in the near future.

Current research interests focuses on:

• In-memory Computing (存内计算)
• Brian-inspired Neuromorphic Computing (神经模态计算)
• Neural Network Acceleration

I'm awarded National Scholarship again!

Sep./27/2022

Our paper "Randomize and Match" has been accepted by ICCD 2022!

Aug./22/2022

I'm awarded Spark Award from HUAWEI!

Jul./29/2022

Fangxin Liu is invited to deliver a talk about SNNs at AI Time

May/25/2022

Our paper "SoBS-X" has been accepted by IEEE TCAD!

Apr./17/2022

Two papers have been accepted by ICMR 2022!

Apr./06/2022

Our paper "L3E-HD" has been accepted by SIGIR 2022!

Apr./01/2022

Our paper "IVQ" has been accepted by IEEE TCAD!

Feb./23/2022

Three papers have been accepted by DAC 2022!

Feb./22/2022

Our paper has been selected by DATE 2022 as Best Paper in the T Track!

Feb./04/2022

Our paper "DynSNN" has been accepted by ICASSP 2022!

Jan./21/2022

Our paper "SpikeConverter" has been accepted by AAAI 2022!

Dec./01/2021

Two papers have been accepted by DATE 2022!

Nov./11/2021

Our paper "SSTDP" has been accepted by Frontiers in Neuroscience!

Oct./01/2021

I'm awarded National Scholarship!

Sep./23/2021

Our paper "HAWIS" has been accepted by ASP-DAC 2022!

Sep./12/2021

Our paper "SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network" has been accepted by ICCD'21 !

Aug./20/2021

Our paper "Improving Neural Network Efficiency via Post-training Quantization with Adaptive Floating-Point" has been accepted by ICCV'21 !

Jul./23/2021

Our paper "Bit-Transformer: Transforming Bit-level Sparsity into Higher Preformance in ReRAM-based Accelerator" has been accepted by ICCAD'21 !

Jul./13/2021

Our paper "IM3A: Boosting Deep Neural Network Efficiency via In-Memory Addressing-Assisted Acceleration" has been accepted by GLSVLSI'21 !

Apr./12/2021

Our paper "PIMGCN: A ReRAM-based PIM Design for Graph Convolutional Network Acceleration" has been accepted by DAC'21 !

Feb./20/2021

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Fangxin Liu, et al. "Randomize and Match: Exploiting Irregular Sparsity for Energy Efficient Processing in SNNs ." to appear in 40th IEEE International Conference on Computer Design (ICCD'22). (Acceptance Rate: 24%).

Fangxin Liu, et al. "SoBS-X: Squeeze-Out Bit Sparsity for ReRAM-Crossbar-Based Neural Network Accelerator.", IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems (IEEE TCAD), 2022. (CCF Tier A). [Link]

Fangxin Liu, et al. "L3E-HD: A Framework Enabling Efficient Ensemble in High-Dimensional Space for Language Tasks.", to appear in 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'22). (Acceptance Rate: 24.7%). [Code] [Link]

Fangxin Liu, et al. "IVQ: In-Memory Acceleration of DNN Inference Exploiting Varied Quantization.", IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems (IEEE TCAD), 2022. (CCF Tier A). [Link]

Fangxin Liu, et al. "EBSP: Evolving Bit Sparsity Patterns for Hardware-Friendly Inference of Quantized Deep Neural Networks." to appear in 59th Design Automation Conference (DAC'22). (Acceptance Rate: 23%). [Link] [Preprint] [Slides]

Fangxin Liu, et al. "SATO: Spiking Neural Network Acceleration via Temporal-Oriented Dataflow and Architecture." to appear in 59th Design Automation Conference (DAC'22). (Acceptance Rate: 23%). [Link] [Preprint] [Slides]

Fangxin Liu, et al. "PIM-DH: ReRAM-based Processing-in-Memory Architecture for Deep Hashing Acceleration." to appear in 59th Design Automation Conference (DAC'22). (Acceptance Rate: 23%). [Link] [Preprint] [Slides]

Fangxin Liu, et al. "DynSNN: A Dynamic Approach to Reduce Redundancy in Spiking Neural Networks." to appear in 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'22). (CCF Tier B). [Link] [Preprint] [Poster]

Fangxin Liu, et al. "SpikeConverter: An Efficient Conversion Framework Zipping the Gap between Artificial Neural Networks and Spiking Neural Networks." to appear in 36th AAAI Conference on Artificial Intelligence (AAAI'22), 2022. (Acceptance Rate: 15%). (Oral, top 5%). [Link] [Poster] [News] [Talk]

Fangxin Liu, et al. "SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training.", Frontiers in Neuroscience, 15 (2021). (Impact Factor 4.2) [Link] [Code]

Fangxin Liu, et al. "SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network." In Proceedings of 39th IEEE International Conference on Computer Design 2021 (ICCD'21). (Acceptance Rate: 24.4%). [Link] [Preprint] [Slides]

Fangxin Liu, et al. "Improving Neural Network Efficiency via Post-training Quantization with Adaptive Floating-Point." In Proceedings of International Conference on Computer Vision 2021 (ICCV'21). (Acceptance Rate: 25.9%). [Link] [Code] [Poster]

Fangxin Liu, et al. "Bit-Transformer: Transforming Bit-level Sparsity into Higher Preformance in ReRAM-based Accelerator." In Proceedings of 40th IEEE/ACM International Conference on Computer-Aided Design (ICCAD'21). (Acceptance Rate: 23.5%). [Link] [Preprint]

Fangxin Liu, et al. "IM3A: Boosting Deep Neural Network Efficiency via In-Memory Addressing-Assisted Acceleration." In Proceedings of 31st Great Lakes Symposium on VLSI 2021 (GLSVLSI'21). (Acceptance Rate: 24%). [Link]

Fangxin Liu, et al. "AUSN: Approximately Uniform Quantization by Adaptively Superimposing Non-uniform Distribution for Deep Neural Networks." CoRR abs/2007.03903 (2020). [Link]

Yongbiao Chen, Fangxin Liu, et al. "TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval." to appear in ACM International Conference on Multimedia Retrieval 2022 (ICMR'22). (Acceptance Rate: 30%). [Link] [Preprint]

Yongbiao Chen, Fangxin Liu, et al. "Supervised Contrastive Vehicle Quantization for Efficient Vehicle Retrieval." to appear in ACM International Conference on Multimedia Retrieval 2022 (ICMR'22). (Acceptance Rate: 30%). [Link]

Zongwu Wang, Fangxin Liu, et al. "Self-Terminated Write of Multi-Level Cell ReRAM for Efficient Neuromorphic Computing." to appear in 25th Design, Automation and Test in Europe Conference (DATE'22). (Best Paper Award). [Link] [Preprint] [Slides]

Tao Yang, Fangxin Liu, et al. "DTQAtten: Leveraging Dynamic Token-based Quantization for Efficient Attention Architecture." to appear in 25th Design, Automation and Test in Europe Conference (DATE'22). (nominated for best paper award). [Link] [Preprint] [Slides]

Qidong Tang, Fangxin Liu, et al. "HAWIS: Hardware-Aware Automated WIdth Search for Accurate, Energy-Efficient and Robust Binary Neural Network on ReRAM Dot-Product Engine." In Proceeding of 27th Asia and South Pacific Design Automation Conference (ASP-DAC'22). (Acceptance Rate: 36.5%). [Link] [Preprint] [Slides]

Tao Yang, Fangxin Liu, et al. "PIMGCN: A ReRAM-based PIM Design for Graph Convolutional Network Acceleration." In Proceeding of Design Automation Conference  (DAC'21). [Link]

National Scholarship, 2022.

National Scholarship, 2021.

Wu Wen Jun Honorary Doctoral Scholarship, 2021.

Best Paper Award from DATE, 2022.

Spark Award from HUAWEI, 2022.

Conference Reviewers

• AAAI Conference on Artificial Intelligence (AAAI), 2023
• Great Lakes Symposium on VLSI (GLSVLSI), 2022
• Design Automation Conference (DAC), 2022
• Great Lakes Symposium on VLSI (GLSVLSI), 2021

Let's read some awesome Quantization / PIM / DeepHash papers.

[NEURO-MIMETIC LEARNING ALGORITHMS]: I am currently creating a neuron-mimetic learning framework, dedicated to serving better accuracy and inference efficiency.

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