Swagath Venkataramani

Publications

• A 7nm 4-Core AI Chip with 25.6TFLOPS Hybrid FP8 Training, 102.4TOPS INT4 Inference and Workload-Aware Throttling

  • • Ankur Agrawal
    • Saekyu Lee
    • et al.
  • 2021
  • ISSCC 2021

  Conference paper

• Value Similarity Extensions for Approximate Computing in General-Purpose Processors

  • • Younghoon Kim
    • Swagath Venkataramani
    • et al.
  • 2021
  • DATE 2021

  Conference paper

• 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

• DyVEDeep: Dynamic Variable Effort Deep Neural Networks

  • • Sanjay Ganapathy
    • Swagath Venkataramani
    • et al.
  • 2020
  • ACM TECS

  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

• Memory and Interconnect Optimizations for Peta-Scale Deep Learning Systems

  • • Swagath Venkataramani
    • Vijayalakshmi Srinivasan
    • et al.
  • 2019
  • HiPC 2019

  Conference paper

• Performance-driven Programming of Multi-TFLOP Deep Learning Accelerators∗

  • • Swagath Venkataramani
    • Jungwook Choi
    • et al.
  • 2019
  • IISWC 2019

  Conference paper