Swagath Venkataramani

Publications

• DeepTools: Compiler and Execution Runtime Extensions for RaPiD AI Accelerator

  • • Swagath Venkataramani
    • Jungwook Choi
    • et al.
  • 2019
  • IEEE Micro

  Paper

• Dynamic Spike Bundling for Energy-Efficient Spiking Neural Networks

  • • Sarada Krithivasan
    • Sanchari Sen
    • et al.
  • 2019
  • ISLPED 2019

  Conference paper

• BiScaled-DNN: Quantizing long-tailed datastructures with two scale factors for deep neural networks

  • • Shubham Jain
    • Swagath Venkataramani
    • et al.
  • 2019
  • DAC 2019

  Conference paper

• SparCE: Sparsity Aware General-Purpose Core Extensions to Accelerate Deep Neural Networks

  • • Sanchari Sen
    • Shubham Jain
    • et al.
  • 2019
  • IEEE TC

  Paper

• 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

  Conference paper

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

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

  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

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

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

  Conference paper

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

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

  Conference paper