Naigang Wang

Publications

• ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training

  • • Chia-Yu Chen
    • Jiamin Ni
    • et al.
  • 2020
  • NeurIPS 2020

  Conference paper

• Ultra-Low Precision 4-bit Training of Deep Neural Networks

  • • Xiao Sun
    • Naigang Wang
    • et al.
  • 2020
  • NeurIPS 2020

  Conference paper

• Efficient AI System Design with Cross-Layer Approximate Computing

  • • Swagath Venkataramani
    • Xiao Sun
    • et al.
  • 2020
  • Proceedings of the IEEE

  Paper

• A 3.0 TFLOPS 0.62V Scalable Processor Core for High Compute Utilization AI Training and Inference

  • • Jinwook Oh
    • Sae Kyu Lee
    • et al.
  • 2020
  • VLSI Circuits 2020

  Conference paper

• Hybrid 8-bit floating point (HFP8) training and inference for deep neural networks

  • • Xiao Sun
    • Jungwook Choi
    • et al.
  • 2019
  • NeurIPS 2019

  Conference paper

• DLFloat: A 16-b Floating Point Format Designed for Deep Learning Training and Inference

  • • Ankur Agrawal
    • Bruce Fleischer
    • et al.
  • 2019
  • ARITH 2019

  Conference paper

• Accumulation bit-width scaling for ultra-low precision training of deep networks

  • • Charbel Sakr
    • Naigang Wang
    • et al.
  • 2019
  • ICLR 2019

  Conference paper

• Innovate Practices on CyberSecurity of Hardware Semiconductor Devices

  • • Alfred L. Crouch
    • Peter Levin
    • et al.
  • 2019
  • VTS 2019

  Conference paper

• Training deep neural networks with 8-bit floating point numbers

  • • Naigang Wang
    • Jungwook Choi
    • et al.
  • 2018
  • NeurIPS 2018

  Conference paper

• A Scalable Multi-TeraOPS Core for AI Training and Inference

  • • Sunil Shukla
    • Bruce Fleischer
    • et al.
  • 2018
  • IEEE SSC-L

  Paper