Program at a Glance


March 18, 2019

(Monday)

 

Time \ Venue Ballroom C, 10F Ballroom D, 11F
08:30~ Registration
09:00-10:30 (90’)

Tutorial 1-1

Connecting ONNX to Proprietary DLAs: An introduction to Open Neural Network Compiler
Luba Tang

Skymizer, Taiwan

Tutorial 2-1

Neuromorphic Artificial Intelligence
Tobi Delbruck
University of Zurich and ETH Zurich Switzerland

10:30-10:45 (15’) Coffee Break
10:45-12:15 (90’)

Tutorial 1-2

Connecting ONNX to Proprietary DLAs: An introduction to Open Neural Network Compiler
Luba Tang
Skymizer, Taiwan

Tutorial 2-2

Neuromorphic Artificial Intelligence
Tobi Delbruck
University of Zurich and ETH Zurich Switzerland

12:15-13:15 (60’) Tutorial Lunch @Ballroom A, 10F
13:15-14:45 (90’)

Tutorial 3

Memory-Centric Chip Architecture for Deep Learning
Sungjoo Yoo
Seoul National University, Korea

Tutorial 4-1

BRAINWAY and Nano-Abacus Architecture: Brain-Inspired Cognitive Computing Using Energy Efficient Physical Computational Structures, Algorithms and Architecture Co-Design
Andreas Andreou
Johns Hopkins University,
USA

14:45-15:00 (15’) Coffee Break
15:00-16:30 (90’)

Tutorial 5

SRAM and RRAM based In-Memory Computing for Deep Learning: Opportunities and Challenges
Jae-sun Seo
Arizona State University, USA

Tutorial 4-2

BRAINWAY and Nano-Abacus Architecture: Brain-Inspired Cognitive Computing Using Energy Efficient Physical Computational Structures, Algorithms and Architecture Co-Design
Andreas Andreou
Johns Hopkins University, USA

16:30-16:40 (10’) Break
16:40-17:00 (20’) Opening Ceremony @Ballroom B, 10F
17:00-18:00 (60’) Keynote #1
Re-Engineering Computing with Neuro-Inspired Learning: Devices, Circuits, and Systems
Kaushik Roy
(Purdue University, USA)
@Ballroom B, 10F
18:00-18:45 (45’) Keynote #2
AI Transforming Hardware Design
Chekib Akrout
(Synopsys, USA)
@Ballroom B, 10F
18:45-21:00 (135’) Welcome Reception @Ballroom A, 10F

 

 

 


 


March 19, 2019

(Tuesday)

 

Time \ Venue Ballroom B, 10F Ballroom C, 10F Ballroom D, 11F
08:00 Registration
08:30-09:30 (60’)

Keynote #3

How Edge AI Technology is Redefining Smart Devices

Ryan Chen
(MediaTek Inc., Taiwan)

   
09:30-10:50 (80’)

Special Session 1

Smart Circuit Techniques for Neural Networks

Lecture session 1

Deep Neural Network for Computer Vision

Lecture session 2

Hardware Accelerators for AI

10:50-11:10 (20’) Coffee Break
11:10-12:30 (80’)

Special Session 2

Edge and Fog Computing to Enabled AI in IoT

Lecture session 3

Neuromorphic Processors

Lecture session 4

Application Specific AI Accelerators

12:30-13:30 (60’) Lunch @Ballroom A, 10F
13:30-15:00 (90’)

Panel Discussion

AI Computing for Smart Life: What, why, who, and where

   
15:00-16:00 (60’)

WICAS & YP

Influences of EDGE Device’s Instant Decision: From Bio-Tech, FinTech to Sustainable Energy & Beyond

   
16:00-16:20 (20’) Coffee Break
16:20-18:00 (100’)

Special Session 3

Analytics Algorithm/Architecture for Smart System Design

Lecture session 5

Deep Learning for Speech and Low-dimensional Signal Processing

 

18:30-20:30 (120’) Banquet @Ballroom A, 10F

 

 


 


March 20, 2019

(Wednesday)

 

Time \ Venue Ballroom B, 10F Ballroom C, 10F Ballroom D, 11F
08:00 Registration
08:30-09:30 (60’)

Keynote #4

Edge Intelligence for Optimized Systems & High-Performance Devices

Anthony Vetro
(MERL, USA)

   
09:30-10:30 (60’)

Special Session/Forum

2018 Low-Power Image Recognition Challenge and Beyond

Lecture session 6

Medical AI (I)

Industrial Session 1

AI computing platform
10:30-10:50 (20’) Coffee Break
10:50-12:10 (80’)

Special Session 4

Intelligent processing of time-series signals

Lecture session 7

Medical AI (II)

Industrial Session 2

Compiler technology for AI chip
12:10-13:10 (60’) Lunch @8F
13:10-14:40 (90’)

Poster session/Live Demo/Showcase
@ Ballroom A, 10F

14:40-16:00 (80’)

Special Session 5
Emerging Memory Technologies for Neuromorphic Circuits and Systems

Lecture session 8

Low Precision Neural Network

 
16:00-16:20 (20’) Coffee Break
16:20-17:40 (80’)

Special Session 6
AI in Advanced Applications

Lecture session 9

Hardware Oriented Neural Network Optimization