Naigang Wang

Publications

• 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

• A Scalable Multi-TeraOPS Deep Learning Processor Core for AI Trainina and Inference

  • • Bruce Fleischer
    • Sunil Shukla
    • et al.
  • 2018
  • VLSI Circuits 2018

  Conference paper

• Novel IC sub-threshold IDDQ signature and its relationship to aging during high voltage stress

  • • Franco Stellari
    • Naigang Wang
    • et al.
  • 2018
  • ESSDERC 2018

  Conference paper

• High-Q magnetic inductors for high efficiency on-chip power conversion

  • • Naigang Wang
    • Bruce Doris
    • et al.
  • 2016
  • IEDM 2016

  Conference paper

• An 82%-efficient multiphase voltage-regulator 3D interposer with on-chip magnetic inductors

  • • Kevin Tien
    • Noah Sturcken
    • et al.
  • 2015
  • VLSI Circuits 2015

  Conference paper