Fan Chen

Orcid: 0000-0001-7928-8331

Affiliations:
  • Indiana University Bloomington, IN, USA
  • Duke University, Department of Electrical and Computer Engineering, Durham, NC, USA (PhD 2020)


According to our database1, Fan Chen authored at least 45 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Links

Online presence:

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Bibliography

2024
LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences.
CoRR, 2024

IoTCO2: Assessing the End-To-End Carbon Footprint of Internet-of-Things-Enabled Deep Learning.
CoRR, 2024

TITAN: A Distributed Large-Scale Trapped-Ion NISQ Computer.
CoRR, 2024

QuantumLeak: Stealing Quantum Neural Networks from Cloud-based NISQ Machines.
Proceedings of the International Joint Conference on Neural Networks, 2024

LLMCarbon: Modeling the End-to-End Carbon Footprint of Large Language Models.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Late Breaking Results: Extracting QNNs from NISQ Computers via Ensemble Learning.
Proceedings of the 61st ACM/IEEE Design Automation Conference, 2024

TITAN: A Fast and Distributed Large-Scale Trapped-Ion NISQ Computer.
Proceedings of the 61st ACM/IEEE Design Automation Conference, 2024

Special Session: End-To-End Carbon Footprint Assessment and Modeling of Deep Learning.
Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis, 2024

JustQ: Automated Deployment of Fair and Accurate Quantum Neural Networks.
Proceedings of the 29th Asia and South Pacific Design Automation Conference, 2024

2023
Sky-Sorter: A Processing-in-Memory Architecture for Large-Scale Sorting.
IEEE Trans. Computers, February, 2023

LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models.
CoRR, 2023

ReFloat: Low-Cost Floating-Point Processing in ReRAM for Accelerating Iterative Linear Solvers.
Proceedings of the International Conference for High Performance Computing, 2023

CryptoQFL: Quantum Federated Learning on Encrypted Data.
Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2023

QDoor: Exploiting Approximate Synthesis for Backdoor Attacks in Quantum Neural Networks.
Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2023

PriML: An Electro-Optical Accelerator for Private Machine Learning on Encrypted Data.
Proceedings of the 24th International Symposium on Quality Electronic Design, 2023

IQGAN: Robust Quantum Generative Adversarial Network for Image Synthesis On NISQ Devices.
Proceedings of the IEEE International Conference on Acoustics, 2023

QTROJAN: A Circuit Backdoor Against Quantum Neural Networks.
Proceedings of the IEEE International Conference on Acoustics, 2023

2022
CryptoLight: An Electro-Optical Accelerator for Fully Homomorphic Encryption.
Proceedings of the 17th ACM International Symposium on Nanoscale Architectures, 2022

QMLP: An Error-Tolerant Nonlinear Quantum MLP Architecture using Parameterized Two-Qubit Gates.
Proceedings of the ISLPED '22: ACM/IEEE International Symposium on Low Power Electronics and Design, Boston, MA, USA, August 1, 2022

Canopy: A CNFET-based Process Variation Aware Systolic DNN Accelerator.
Proceedings of the ISLPED '22: ACM/IEEE International Symposium on Low Power Electronics and Design, Boston, MA, USA, August 1, 2022

MOCCA: A Process Variation Tolerant Systolic DNN Accelerator using CNFETs in Monolithic 3D.
Proceedings of the GLSVLSI '22: Great Lakes Symposium on VLSI 2022, Irvine CA USA, June 6, 2022

2021
FeFET-based Process-in-Memory Architecture for Low-Power DNN Training.
Proceedings of the IEEE/ACM International Symposium on Nanoscale Architectures, 2021

PUFFIN: An Efficient DNN Training Accelerator for Direct Feedback Alignment in FeFET.
Proceedings of the IEEE/ACM International Symposium on Low Power Electronics and Design, 2021

Rerec: In-ReRAM Acceleration with Access-Aware Mapping for Personalized Recommendation.
Proceedings of the IEEE/ACM International Conference On Computer Aided Design, 2021

RECOIN: A Low-Power Processing-in-ReRAM Architecture for Deformable Convolution.
Proceedings of the GLSVLSI '21: Great Lakes Symposium on VLSI 2021, 2021

Marvel: A Vertical Resistive Accelerator for Low-Power Deep Learning Inference in Monolithic 3D.
Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2021

RAISE: A Resistive Accelerator for Subject-Independent EEG Signal Classification.
Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2021

2020
In-Memory Computing Architecture for Deep Learning Acceleration.
PhD thesis, 2020

Low-Cost Floating-Point Processing in ReRAM for Scientific Computing.
CoRR, 2020

AccPar: Tensor Partitioning for Heterogeneous Deep Learning Accelerators.
Proceedings of the IEEE International Symposium on High Performance Computer Architecture, 2020

ReBoc: Accelerating Block-Circulant Neural Networks in ReRAM.
Proceedings of the 2020 Design, Automation & Test in Europe Conference & Exhibition, 2020

Parallelism in Deep Learning Accelerators.
Proceedings of the 25th Asia and South Pacific Design Automation Conference, 2020

PARC: A Processing-in-CAM Architecture for Genomic Long Read Pairwise Alignment using ReRAM.
Proceedings of the 25th Asia and South Pacific Design Automation Conference, 2020

2019
Exploring Bit-Slice Sparsity in Deep Neural Networks for Efficient ReRAM-Based Deployment.
Proceedings of the Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing, 2019

How to Obtain and Run Light and Efficient Deep Learning Networks.
Proceedings of the International Conference on Computer-Aided Design, 2019

Deep Learning for Vertex Reconstruction of Neutrino-nucleus Interaction Events with Combined Energy and Time Data.
Proceedings of the IEEE International Conference on Acoustics, 2019

Efficient Process-in-Memory Architecture Design for Unsupervised GAN-based Deep Learning using ReRAM.
Proceedings of the 2019 on Great Lakes Symposium on VLSI, 2019

ZARA: A Novel Zero-free Dataflow Accelerator for Generative Adversarial Networks in 3D ReRAM.
Proceedings of the 56th Annual Design Automation Conference 2019, 2019

Taming extreme heterogeneity via machine learning based design of autonomous manycore systems.
Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis Companion, 2019

2018
Design and Data Management for Magnetic Racetrack Memory.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2018

EMAT: an efficient multi-task architecture for transfer learning using ReRAM.
Proceedings of the International Conference on Computer-Aided Design, 2018

Exploring the opportunity of implementing neuromorphic computing systems with spintronic devices.
Proceedings of the 2018 Design, Automation & Test in Europe Conference & Exhibition, 2018

ReRAM-based accelerator for deep learning.
Proceedings of the 2018 Design, Automation & Test in Europe Conference & Exhibition, 2018

ReGAN: A pipelined ReRAM-based accelerator for generative adversarial networks.
Proceedings of the 23rd Asia and South Pacific Design Automation Conference, 2018

Process variation aware data management for magnetic skyrmions racetrack memory.
Proceedings of the 23rd Asia and South Pacific Design Automation Conference, 2018


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