Swagath Venkataramani

Publications

• A Compiler for Deep Neural Network Accelerators to Generate Optimized Code for a Wide Range of Data Parameters from a Hand-crafted Computation Kernel

  • • Eri Ogawa
    • Kazuaki Ishizaki
    • et al.
  • 2019
  • COOL CHIPS 2019

• Data Subsetting: A Data-Centric Approach to Approximate Computing

  • • Younghoon Kim
    • Swagath Venkataramani
    • et al.
  • 2019
  • DATE 2019

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

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

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

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

• DyHard-DNN: Even more DNN acceleration with dynamic hardware reconfiguration

  • • Mateja Putic
    • Alper Buyuktosunoglu
    • et al.
  • 2018
  • DAC 2018

• Compensated-DNN: Energy efficient low-precision deep neural networks by compensating quantization errors

  • • Shubham Jain
    • Swagath Venkataramani
    • et al.
  • 2018
  • DAC 2018

• Exploiting approximate computing for deep learning acceleration

  • • Chia-Yu Chen
    • Jungwook Choi
    • et al.
  • 2018
  • DATE 2018

• Very low voltage (VLV) design

  • • Ramon Bertran
    • Pradip Bose
    • et al.
  • 2017
  • ICCD 2017

• POSTER: Design Space Exploration for Performance Optimization of Deep Neural Networks on Shared Memory Accelerators

  • • Swagath Venkataramani
    • Jungwook Choi
    • et al.
  • 2017
  • PACT 2017

• Scaledeep: A scalable compute architecture for learning and evaluating deep networks

  • • Swagath Venkataramani
    • Ashish Ranjan
    • et al.
  • 2017
  • ISCA 2017