The 2ndInternational Symposium
on AI Electronics
Organized by WISE Program for AI Electronics

Venue2021

Feb.

15OnlineMon.

DateTechnical Program
10 : 00 − 10 : 10

10 : 10 − 11 : 00

11 : 10 − 12 : 00

Opening Session

Conversational AI
becoming mainstream

Human Perspective Scene
Understanding via
Multimodal Sensing

10 : 00 − 10 : 05

Welcome Address
Hideo Ohno

President, Tohoku University

Keynote10 : 05 − 10 : 10Opening remarks
Toshiro Kaneko

Program Coordinator,
WISE Program for AI Electronics,Tohoku University

KeynoteAlex Acero

Chiori Hori

Senior Director, Apple

Senior Principal Research Scientist,Mitsubishi Electric Research Laboratories

P02

12 : 00 − 13 : 30

P03

13 : 30 − 14 : 00

14 : 00 − 14 : 30

14 : 30 − 15 : 00

Brainmorphic
computing hardware
paradigm for edge AI

Real-world Intelligent
Systems andEnergy-Efficient
Accelerators

Searching for rareatomic nuclei via image
analysis using machinelearning

Lunch

InvitedYoshihiko
Horio
Professor, Research Institute of Electrical Communication,
Tohoku University

P04

InvitedInvitedMasanoriHariyama

Junya
YoshidaProfessor, Graduate School of Information Sciences,
Tohoku University

P05

Assistant Professor, Graduate School of Science, Tohoku University
Visiting Scientist, High Energy Nuclear Physics Laboratory, RIKEN

P06

15 : 15 − 15 : 55

16 : 00 − 16 : 50

Human resource development
cost as an essential investment forestablishing sustainable society

Computer Vision:
Geometry, Uncertainty andDeep Learning

KeynoteP07

KeynoteHiroshi Amano

Roberto Cipolla

Professor, Institute of Materials and Systems for Sustainability
Center for Integrated Research of Future Electronics,
Nagoya University

Professor of Information Engineering,
University of Cambridge

P08

Keynote

Conversational AI becoming mainstream
Alex AceroSenior Director, AppleABSTRACT
After decades in the lab, artiﬁcial intelligence is becoming part of usersʼ everyday lives. Neural networks
run every time you unlock your iPhone with your face, or when you interact with Siri, the ﬁrst mainstream
intelligent assistant. This talk will introduce how advances in research were needed to improve the userexperience.

BIOGRAPHYAlexAceroAlex Acero is Sr. Director at Apple leading speech recognition, speech synthesis, language understanding, anddialog for Siri, Apple ʼ s personal assistant for iPhone, iPad, Apple Watch, Apple TV, Carplay, Macintosh, andHomePod. Prior to joining Apple in 2013, he spent 20 years at Microsoft Research managing teams in speech, audio,multimedia, computer vision, natural language processing, machine translation, machine learning, and information
retrieval. His team at Microsoft Research built Bing Translator, contributed to Xbox Kinect, and pioneered the use ofdeep learning in large vocabulary speech recognition. From 1991-1993 he managed the speech team for Spain ʼ s
Telefonica. His ﬁrst stint at Apple started in 1990. He is Affiliate Faculty at the University of Washington.
Dr. Acero is a Fellow of IEEE and ISCA. He received the 2017 Norbert Wiener Society Award, the 2013 Best Paper
Award, and the 2006 Distinguished Lectureship from the IEEE Signal Processing Society. Alex received anengineering degree from the Polytechnic University of Madrid, a Masters from Rice University, and a PhD fromCarnegie Mellon. Alex is author of the textbook “Spoken Language Processing” , over 250 technical papers and 150
US patents. Alex has served IEEE in many roles including the IEEE Board of Directors, the IEEE Foundation Board,and President of the IEEE Signal Processing Society.

02Keynote

Human Perspective Scene Understanding via
Multimodal Sensing
Chiori Hori

Senior Principal Research Scientist,Mitsubishi Electric Research Laboratories

ABSTRACTThe recent artiﬁcial intelligence (AI) boom and intelligent use of data acquired from various sensors has
certainly accelerated the development of technologies needed to realize advanced human-like capabilities
in machines. AI technologies have come a long way in accurately perceiving visual scenes andunderstanding speech --- even achieving super-human performance on certain tasks, e.g., speech
recognition for overlapped utterances spoken by multilingual speakers. However, one important piece oftechnology is still missing: natural and context-aware human-machine interaction, where machinesunderstand their surrounding scene from the human perspective and they are able to share theirunderstanding with humans using natural language. Currently, there are no mechanisms for machines tohave a conversation about a surrounding event or experience with humans using natural language. Tobridge this communications gap, we have developed and built a new AI system, called Scene-Aware
Interaction, enables machines to translate their perception and understanding of a scene and respond to it
using natural language to interact more effectively with humans.

BIOGRAPHYChioriHoriDr. Chiori Hori is a Senior Principal Research Scientist at Mitsubishi Electric Research Laboratories (MERL) in Boston
since 2015. Her work is focused on multimodal scene-aware interaction technologies toward human-robot
communications. She spent 8 years at NICT prior to MERL after researching at CMU and NTT Communication ScienceLaboratories. She led the project for the worldʼ s ﬁrst open-domain QA system which enabled real-time over-a-million
word vocabulary continuous speech recognition at NTT in 2004. She was the director of the Spoken Language
Communication Laboratory of NICT and built a WFST-based speech recognition system which has been ranked ﬁrst placein the competition of speech recognition performance on English TED speech at IWSLT in three consecutive years since2012. The NICT speech recognition system has also been utilized for commercial products such as spoken dialog systemsof NTT DOCOMO and speech translation systems of KDDI AU on smartphones. She received several awards including thePaper Award from IEICE in 2003, the 24th TELECOM System Technology Award by the Telecommunications Advancement
Foundation in 2009, the International Cooperation Award from ITU-AJ in 2012, DOCOMO Mobile Science Award for Social
Science Sector from Mobile Communication Fund in 2012 and the 58th Maejima Hisoka Award, Tsushinbunka Association,2013. She has served on the editorial board of "Computer Speech and Language" and is a technical committee member of"Speech and Language Processing Group" of IEEE Signal Processing Society. She received BS and MS degrees fromYamagata University and Ph.D. degrees from Tokyo Institute of Technologies, respectively.

03

Invited

Brainmorphic computing hardware
paradigm for edge AI
Yoshihiko Horio

Professor, Research Institute of Electrical Communication,Tohoku UniversityABSTRACT
In order to realize near future edge AI, which provides personal and private services, ultra-compact andultra-low power unobtrusive peri-personal space edge hardware is required. For such edge devices,
adaptations to the user and environment with real-time continuous on-line learning, immediate responses,
and non-volatile memory are mandatory. Therefore, to implement the space and power efficientperi-personal leaning edge AI devices, we need a novel non-von Neumann-type computational principle,architecture, and device. One of promising candidates is a brainmorphic computational paradigm withbrain-speciﬁc computational schemes such as consciousness/sub-consciousness, prediction, inference,memory, attention, and self. In this talk, I introduce the brainmorphic computing paradigm and its
hardware architecture. The brainmorphic computing hardware system should process information
imitating the anatomical and physiological mechanisms of the brain by naturally using physical anddynamical characteristics of the constituent devices, especially through nonlinear analog circuits anddevices. The latest knowledge from brain science, especially, on high-order brain speciﬁc functions
emerged from high-dimensional complex neuro-dynamics, are reﬂected in the design of brainmorphic
hardware. In addition, the bodily and environmental constraints are considered and utilized as embodimentin this hardware paradigm. Our efforts on physical implementation of the brainmorphic computing
hardware are illustrated; for example, spin-torque orbit devices for neuronal and synaptic dynamics, and achaotic neural network reservoir for computation through high-dimensional complex dynamics.

BIOGRAPHY

Yoshihiko
Horio

Yoshihiko Horio currently is a professor at Research Institute of Electrical Communication, Tohoku
University. He received the B.E., M.E., and Ph.D. degrees in electrical engineering from Keio University,
Yokohama, Japan, in 1982, 1984, and 1987, respectively. From 1987 to 2015, he was with Tokyo Denki
University where he was a professor from 2000. He is a Honorary Professor of Tokyo Denki University from2020. From April 1992 to March 1994, he was a Visiting Professor at Center for Telecommunications
Research, Columbia University, New York, U.S.A. From 1999 to 2002, he was a vice president of EpochTechnologies, LLC, U.S.A. He was a President of NOLTA Society, the Institute of Electronics, Information
and Communication Engineers (IEICE). He received the Lifetime Achievement Award, NOLTA Society,IEICE in 2016. He is currently Fellow, IEICE, Director of Japanese Neural Network Society, andEditor-in-Chief of Nonlinear Theory and Its Applications, IEICE journal. His current research interest
includes neuromorphic and brainmorphic hardware systems, VLSI information processing systems basedon complex physical dynamics, and high-order brain-inspired VLSI systems with consciousness, self, andembodiment.

04

Invited

Real-world Intelligent Systems andEnergy-Efficient Accelerators
Masanori Hariyama

Professor, Graduate School of Information Sciences,Tohoku UniversityABSTRACT
In order to realize intelligent systems working in real world environment, we advance researches from two
type of approaches. One is to develop intelligent algorithms/systems, and another is to develop
high-performance and low-power accelerators for the intelligent algorithms, since intelligent algorithms
requires a huge computational amount that exceeds the computational capability of conventional
computers. In this presentation, we introduce our development examples of intelligent systems such as
medical information system, IoT-based system to quantify child development, Material informatics, etc.
Moreover, we introduce custom supercomputers for intelligent systems such as AI-based natural language
processing, quantum annealing simulations to solve combinatorial optimization problems, etc.

BIOGRAPHY
Masanori Hariyama received the B.E. degree in electronic engineering, the M.S. degree in information
sciences, and the Ph.D. degree in information sciences from Tohoku University, Sendai, Japan, in 1992,
1994, and 1997, respectively. He is currently a Professor with the Graduate School of Information Sciences in
Tohoku University. His research interests include real-world applications such as robotics and medical
information systems, big-data analysis such as human-behavior analysis, high-performance computing
such as quantum computing simulation.

MasanoriHariyama05

Invited

Searching for rare atomic nuclei via image
analysis using machine learning
Junya Yoshida

Assistant Professor, Graduate School of Science, Tohoku University
Visiting Scientist, High Energy Nuclear Physics Laboratory, RIKEN

ABSTRACT
We are studying rare atomic nuclei called “hypernuclei” that consist of protons, neutrons, and other typesof particles. They can be produced with beams from high-intensity particle accelerators, but quite rarely.
Although they are unstable with a lifetime of a billionth of a second, they are important for understanding
interactions among the particles which make up atomic nuclei and a dense stellar object called a neutronstar. To search for a special type of hypernuclei ( “ double hypernuclei ” ), we have used dedicatedphotographic sheets, irradiating them with beams and recording “events” of production and decay of
double hypernuclei. Optical micrographs of the recorded image with sub-micrometer spatial resolution
allows us to identify double hypernuclei. We have found a few instances of uniquely identiﬁed doublehypernuclei, but the observed number is limited due to their small production probability, hugebackground, and loads of visual inspection. It is expected that deep mining of the images in the irradiatedphotographic sheets shall reveal a thousand of double hypernuclear events. To achieve the deep mining,
we introduced machine learning for image analysis. One of the major challenges is image synthesis tocompensate for the lack of training data. We developed a method to produce training data with physics
simulations and a GAN. Another challenge is on evaluating the performance of machine learning models.
We used several kinds of typical events recorded in the photographic sheets to evaluate the performanceof the developed models. We will discuss the progress and outlook of our development.

BIOGRAPHY

JunyaYoshida

Junya Yoshida is an assistant professor in the Graduate School of Science at Tohoku University and avisiting scientist in the High Energy Nuclear Physics Laboratory at RIKEN. He is currently stationed atRIKEN. His current research interests are on experimental nuclear physics using nuclear emulsions andmachine learning. His main research activities are on exotic nuclei with strange-quarks, so-called
hypernuclei. He contributed to the ﬁrst observation of a candidate of a Xi hypernucleus, and he received
the 22nd Outstanding Paper Award of the Physical Society of Japan as a co-author of the paper reporting
the observation. He is also interested in the application of the nuclear emulsion technique to other ﬁelds.
Particularly, he is currently developing detectors to measure precisely thermal or cold neutron beams fornon-destructive inspections. He obtained a Ph.D. degree from the Department of Physics at Nagoya
University in 2013. Following the doctoral study, he worked as a postdoc researcher at Gifu University from2013 to 17 and the Advanced Science Research Center at Japan Atomic Energy Agency from 2017 to 20.

06

Keynote

Human resource development cost as an essential investment
forestablishing sustainable society
Hiroshi Amano

Professor, Institute of Materials and Systems for Sustainability
Center for Integrated Research of Future Electronics, Nagoya University

ABSTRACTOur DII Collaborative Graduate Program for Accelerating Innovation in Future Electronics (DII program) has been ongoingsince 2018. The DII programs aim to foster a cluster of talent who can accomplish product innovation within 10 years,
which previously required 30 years or longer. This program is designed to nurture three types of human resources whowill play different roles in continually creating product innovation in future electronics. Believing that cooperation andcollaboration between these different types of personnel toward a common goal is the key to accelerating innovation,
we call this partnership the DII Collaboration, which is based on the names of the three types of personnel.
Deployers : Conceptualize and plan the creation of social values through innovative products.
Innovators : Anticipate the ﬁnal product from seeds, resolve technical challenges in the development of the product,and bring the process to completion.
Investigators : Ingeniously clarify social issues and propose a solution based on their deep insight.
A key feature of this program is the involvement of mentors from industry: not only from manufacturing-based
companies, but also from entrepreneurial support companies and national research institutes. The degree offered bythe DII program is independent of the conventional Ph.D. program. The DII program provides an unrivaled opportunity forenthusiastic students who want to apply their knowledge to realize innovation, and the entire staff of the DII program isproud to provide this program to young students.

BIOGRAPHY
Hiroshi Amano received his BE, ME and DE degree in 1983, 1985 and 1989, respectively, from Nagoya University. From1988 to 1992, he was a research associate at Nagoya University. In 1992, he moved to Meijo University, where he was an
assistant professor, associate professor from 1998 till 2002, and professor from 2002 till 2010. He moved to Nagoya
University, where he was a professor of Graduate School of Engineering from 2011 till 2015. On Oct. 1, 2015, he became adirector of Center for Integrated Research of Future Electronics, Institute of Materials and Systems for Sustainability,Nagoya University. He has also been the director of the Akasaki Research Center, Nagoya University since 2011.

Hiroshi
AmanoDuring his doctoral program at the Nagoya University Graduate School of Engineering, he was able to realize
high-quality epitaxially grown GaN ﬁlm with metal-organic vapor phase epitaxy, p-type GaN ﬁlm doped with Mg while
conducting research with Professor Akasaki. For the ﬁrst time in history, he established the technology necessary forthe production of blue LEDs, thus performing a great achievement the development of the high-luminosity blue LED.
He is currently developing energy-saving technologies for the fabrication of efficient high-power/high-frequency
semiconductor devices at Nagoya University. Prof. Amano shared the Nobel Prize in Physics 2014 with Prof. Isamu
Akasaki and Prof. Shuji Nakamura "for the invention of efficient blue light-emitting diodes which has enabled bright andenergy-saving white light sources".

07

Keynote

Computer Vision: Geometry, Uncertainty and Deep Learning
Roberto Cipolla

Professor of Information Engineering,
University of Cambridge

ABSTRACTThe last decade has seen a revolution in the theory and application of computer vision and machinelearning. I will begin with a brief review of some of the fundamentals with a few examples of thereconstruction, registration and recognition of three-dimensional objects and their translation into novel
commercial applications. I will then introduce some recent results from real-time deep learning systems
that exploit geometry and compute model uncertainty. Understanding what a model does not know is a
critical part of safe machine learning systems. New tools, such as Bayesian deep learning, provide aframework for understanding uncertainty in deep learning models, aiding interpretability and safety ofsuch systems. Additionally, knowledge of geometry is an important consideration in designing effective
algorithms. In particular, we will explore the use of geometry to help design networks that can be trainedwith unlabeled data for stereo and for human body pose and shape recovery.

BIOGRAPHY

RobertoCipolla

Professor Cipolla has been a Professor of Information Engineering at the University of Cambridge since2000. Previously he worked as a Toshiba Fellow and engineer at the Toshiba Corporation Research andDevelopment Centre in Kawasaki, Japan, and was awarded a D.Phil. (Computer Vision) from the Universityof Oxford in 1991. Roberto ʼ s research interests are in computer vision and robotics and include therecovery of motion and 3D shape of visible surfaces from image sequences; object detection andrecognition; novel man-machine interfaces using hand, face and body gestures; real-time visual tracking
for localisation and robot guidance; applications of computer vision in mobile phones, visual inspection
and image-retrieval and video search.

08

Contact

WISE program for AI Electronics Ofﬁce
Room 404, Building 2, Group of Electrical, Information and Physics Engineering
6-6-05, Aramaki Aza Aoba, Aoba-ku, Sendai, Miyagi 980-8579, Japan

TEL +81-22-795-5667 MAIL aie-office@grp.tohoku.ac.jp