2019 International Conference On Computer Aided Design

The Premier Conference Devoted to Technical Innovations in Electronic Design Automation

November 4-7, 2019The Westin Westminster Westminster, CO

v class="event-details"> MP Associates, Inc.

MONDAY November 05, 4:15pm - 5:45pm | Monte Carlo

Sweet Memories of Deep Learning
Yanzhi Wang - Northeastern Univ.
Who doesn't remember the basics of deep learning! But can you design systems for in-memory computing using FeFETs that reduce power consumption and ReRAM that moderates data migration? The first paper in this session investigates reliable ReRAM-based deep learning with single-bit cells. The second paper introduces a power-efficient FeFET-based in-memory computing architecture, whereas the third paper designs and implements a system for multitask (transfer) learning using GPUs and ReRAM. Even as you learn new things, important information should stay in your memory!

3B.1DL-RSIM: A Simulation Framework to Enable Reliable ReRAM-Based Accelerators for Deep Learning
 Speaker: Hsiang-Yun Cheng - Academia Sinica
 Authors: Meng-Yao Lin - National Taiwan Univ.
Hsiang-Yun Cheng - Academia Sinica
Wei-Ting Lin - National Taiwan Univ.
Tzu-Hsien Yang - National Taiwan Univ.
I-Ching Tseng - National Taiwan Univ.
Chia-Lin Yang - National Taiwan Univ.
Han-Wen Hu - Macronix International Co., Ltd.
Hung-Shen Chang - Macronix International Co., Ltd.
Hsiang-Pang Li - Macronix International Co., Ltd.
Meng-Fan Chang - National Tsing Hua Univ.
3B.2A Ferroelectric FET Based Power-Efficient Architecture for Data-Intensive Computing
 Speaker: Saibal Mukhopadhyay - Georgia Institute of Technology
 Authors: Yun Long - Georgia Institute of Technology
Taesik Na - Georgia Institute of Technology
Prakshi Rastogi - Georgia Institute of Technology
Karthik Rao - Georgia Institute of Technology
Asif Islam Khan - Georgia Institute of Technology
Sudhakar Yalamanchili - Georgia Institute of Technology
Saibal Mukhopadhyay - Georgia Institute of Technology
3B.3EMAT: An Efficient Multi-Task Architecture for Transfer Learning Using ReRAM
 Speaker: Fan Chen - Duke Univ.
 Authors: Fan Chen - Duke Univ.
Hai Li - Duke Univ.