7 citations · 16 across the 11 of their papers we have counts for
6 papers
Towards Efficient Neuro-Symbolic AI: From Workload Characterization to Hardware Architecture
Zishen Wan, Che-Kai Liu, Hanchen Yang +13
The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, are facing challenges surrounding unsustainable computational trajectories, l…
MG-Verilog: Multi-grained Dataset Towards Enhanced LLM-assisted Verilog Generation
Yongan Zhang, Zhongzhi Yu, Yonggan Fu +2
Large Language Models (LLMs) have recently shown promise in streamlining hardware design processes by encapsulating vast amounts of domain-specific data. In addition, they allow us…
Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI
Zishen Wan, Che-Kai Liu, Hanchen Yang +7
The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, have significantly impacted various aspects of our lives. However, the curren…
NetDistiller: Empowering Tiny Deep Learning via In-Situ Distillation
Shunyao Zhang, Yonggan Fu, Shang Wu +4
Boosting the task accuracy of tiny neural networks (TNNs) has become a fundamental challenge for enabling the deployments of TNNs on edge devices which are constrained by strict li…
e-G2C: A 0.14-to-8.31 J/Inference NN-based Processor with Continuous On-chip Adaptation for Anomaly Detection and ECG Conversion from EGM
Yang Zhao, Yongan Zhang, Yonggan Fu +10
This work presents the first silicon-validated dedicated EGM-to-ECG (G2C) processor, dubbed e-G2C, featuring continuous lightweight anomaly detection, event-driven coarse/precise c…
MIA-Former: Efficient and Robust Vision Transformers via Multi-grained Input-Adaptation
Zhongzhi Yu, Yonggan Fu, Sicheng Li +2
ViTs are often too computationally expensive to be fitted onto real-world resource-constrained devices, due to (1) their quadratically increased complexity with the number of input…