Zihan Zou

Orcid: 0009-0002-1510-1341

According to our database1, Zihan Zou authored at least 12 papers between 2023 and 2025.

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

Timeline

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Bibliography

2025
PF²A-ViT: Parameter-Free and Feature-Aware Dynamic Token Pruning Accelerator With Complementary Quantization-Encoding for Vision Transformer.
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., November, 2025

A Universal Deep Learning Force Field for Molecular Dynamic Simulation and Vibrational Spectra Prediction.
CoRR, October, 2025

QMe14S, A Comprehensive and Efficient Spectral Dataset for Small Organic Molecules.
CoRR, January, 2025

OutlierCIM: Outlier-Aware Digital CIM-Based LLM Accelerator with Hybrid-Strategy Quantization and Unified FP-INT Computation.
Proceedings of the 62nd ACM/IEEE Design Automation Conference, 2025

A 52.03TOPS/W DCIM-Based Accelerator with FlashAttention and Sparsity-Aware Alignment for LLMs.
Proceedings of the IEEE Custom Integrated Circuits Conference, 2025

TWDP: A Vision Transformer Accelerator with Token-Weight Dual-Pruning Strategy for Edge Device Deployment.
Proceedings of the 30th Asia and South Pacific Design Automation Conference, 2025

2024
Layer-Sensitive Neural Processing Architecture for Error-Tolerant Applications.
IEEE Trans. Very Large Scale Integr. Syst., May, 2024

Asymptotic stability of neutral stochastic pantograph systems with Markovian switching.
Int. J. Control, 2024

Live Demonstration: A Target-Separable BWN Inspired Speech Recognition Processor with Low-power Precision-adaptive Approximate Computing.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2024

An Index-free Coding Compression Mechanism for Vision Transformers.
Proceedings of the IEEE International Conference on Integrated Circuits, 2024

FDCA: Fine-grained Digital-CIM based CNN Accelerator with Hybrid Quantization and Weight-Stationary Dataflow.
Proceedings of the 61st ACM/IEEE Design Automation Conference, 2024

2023
A deep learning model for predicting selected organic molecular spectra.
Nat. Comput. Sci., 2023


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