Qinyu Chen

Assistant Professor

Leiden University

Netherlands

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Qinyu Chen is an Assistant Professor at Leiden University in the Netherlands. His research focuses on developing energy-efficient neural network accelerators and bio-inspired computing, as reflected in his recent publications, including advancements in wearable ECG applications and digital keyword spotting integrated circuits. He also explores innovative approaches in eye tracking for AR/VR devices and the implementation of spiking neural networks for various applications.

Articles (16)

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2026

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2025

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2022

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2022

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2022

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2022

Collaborators (4)

Chang Gao

Assistant Professor

Delft University of Technology

NETHERLANDS

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Yizhuo Wu

Delft University of Technology

NETHERLANDS

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Morteza S. Alavi

Assistant Professor

Delft University of Technology

NETHERLANDS

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Longbiao Cheng

ETH Zurich (Swiss Federal Institute of Technology)

SWITZERLAND

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Qinyu Chen

Research Interests

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About

Articles (16)

CardioLike-Net: An edge-end inter-patient arrhythmia classifier with quantization-aware-training for wearable ECG applications

DeltaKWS: A 65nm 36nJ/Decision Bio-Inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC With 0.6V Near-Threshold SRAM

An 8.62-μW 75-dB DRSoC Fully Integrated SoC for Spoken Language Understanding

DeltaDPD: Exploiting Dynamic Temporal Sparsity in Recurrent Neural Networks for Energy-Efficient Wideband Digital Predistortion

A 1.1 μJ/Inference Binary Spiking Neural Network Accelerator for DVS Gesture Recognition

Toward Efficient Eye Tracking in AR/VR Devices: A Near-Eye DVS-Based Processor for Real-Time Gaze Estimation

MP-DPD: Low-Complexity Mixed-Precision Neural Networks for Energy-Efficient Digital Predistortion of Wideband Power Amplifiers

An Artificial Spiking Afferent Nerve Based on Synaptic Transistor for Thermal Perception

An Energy Efficient Residual Spiking Neural Network Accelerator With Ternary Spikes

HAS-RL: A Hierarchical Approximate Scheme Optimized With Reinforcement Learning for NoC-Based NN Accelerators

To Spike or Not to Spike: A Digital Hardware Perspective on Deep Learning Acceleration

Ultralow-power spiking neural networks for 1024-ary orbital angular momentum shift keying free-space optical communication

Cerebron: A Reconfigurable Architecture for Spatiotemporal Sparse Spiking Neural Networks

Skydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance

Skydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance

Cerebron: A Reconfigurable Architecture for Spatiotemporal Sparse Spiking Neural Networks

Collaborators (4)

Chang Gao

Yizhuo Wu

Morteza S. Alavi

Longbiao Cheng

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