Work

Real-time cell sorting with scalable in situ FPGA-accelerated deep learning

Year: 2025

Type: article

Abstract: In situ FPGA deployment of knowledge-distilled CNNs achieves 14.5 µs inference for real-time label-free cell sorting with 40× faster detection-to-trigger time than SOTA, enabling clinical-grade lymphocyte classification via 5000-fold compression.

Source: Digital Discovery

DOI: 10.1039/d5dd00345h

Authors: Khayrul Islam, Ryan F. Forelli, Jianzhong Han, Deven Bhadane, Jian Huang, Joshua C. Agar, Nhan Tran, Seda Ogrenci, Yaling Liu

Institutions: Lehigh University, Northwestern University, Coriell Institute For Medical Research, Cooper Medical School of Rowan University, Temple University, Drexel University, Fermi National Accelerator Laboratory, Sichuan University, West China Hospital of Sichuan University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Science Foundation, Northwestern University, International Institute for Nanotechnology, Northwestern University, Lawrence Livermore National Laboratory

Awards: POSE: Phase II: An Open Source Ecosystem for Collaborative Rapid Design of Edge AI Hardware Accelerators for Integrated Data Analysis and Discovery, MRI: Development of Heterogeneous Edge Computing Platform for Real-Time Scientific Machine Learning

OpenAlex ID: W4416573548

FWCI: 1.078

Cites: 34

Cited by: 3

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