Challenges and Contributing Factors in the Utilization of Large Language Models (LLMs)

no code implementations • 20 Oct 2023 • Xiaoliang Chen, Liangbin Li, Le Chang, Yunhe Huang, Yuxuan Zhao, Yuxiao Zhang, Dinuo Li

To address these issues, it's suggested to diversify training data, fine-tune models, enhance transparency and interpretability, and incorporate ethics and fairness training.

Ethics Fairness +1

Memory-Efficient CNN Accelerator Based on Interlayer Feature Map Compression

no code implementations • 12 Oct 2021 • Zhuang Shao, Xiaoliang Chen, Li Du, Lei Chen, Yuan Du, Wei Zhuang, Huadong Wei, Chenjia Xie, Zhongfeng Wang

To maintain real-time processing in embedded systems, large on-chip memory is required to buffer the interlayer feature maps.

Feature Compression Quantization

A multilayer interstitial fluid flow along vascular adventitia

no code implementations • 23 Sep 2020 • Hongyi Li, You Lv, Xiaoliang Chen, Bei Li, Qi Hua, Fusui Ji, Yajun Yin, Hua Li

In real-time observations, the calculated velocity of a continuous ISF flow along fibers of a PACT pathway was 3. 6-15. 6 mm/sec.

DeepRMSA: A Deep Reinforcement Learning Framework for Routing, Modulation and Spectrum Assignment in Elastic Optical Networks

no code implementations • 6 May 2019 • Xiaoliang Chen, Baojia Li, Roberto Proietti, Hongbo Lu, Zuqing Zhu, S. J. Ben Yoo

To overcome the instability issue in the training of DeepRMSA-EP due to the oscillations of cumulative rewards, we further propose a window-based flexible training mechanism, i. e., DeepRMSA-FLX.

Blocking

An Analog Neural Network Computing Engine using CMOS-Compatible Charge-Trap-Transistor (CTT)

no code implementations • 19 Sep 2017 • Yuan Du, Li Du, Xuefeng Gu, Jieqiong Du, X. Shawn Wang, Boyu Hu, Mingzhe Jiang, Xiaoliang Chen, Junjie Su, Subramanian S. Iyer, Mau-Chung Frank Chang

The proposed computing engine is composed of a scalable CTT multiplier array and energy efficient analog-digital interfaces.