Informatik, TU Wien

Events of the DC-RES

“The Journey of Research in Computing:
Basic versus Applied Research – Specialization versus Multidisciplinarity”

The Doctoral College Resilient Embedded Systems (DC-RES) invites to the following event:

“The Journey of Research in Computing:
Basic versus Applied Research – Specialization versus Multidisciplinarity”

 

Keynote-talk and panel discussion


When : 12.12.2018, 17:00 s.t. - 19:00
Where: Lecture room EI10 Fritz Paschke, Gußhausstrasse 27-29, 1040 Wien, Stg. 1, ground floor

Program:

1. Opening

Prof. Sabine Seidler, Rector TU Wien

2. Introduction of the Doctoral College “Resilient Embedded Systems”:

Prof. Andreas Steininger (Director of the Vienna PhD School of Informatics & Chairperson of the Doctoral College)
and
FH-Prof. Christian Kollmitzer (Vice Rector FH Technikum Wien, Program Director Electronics and Business | Industrial Electronics)
will introduce the Doctoral College and the Collegiates.

3. Keynote-talk: moderated by Josef Broukal

Prof. Kaushik Roy, Director of the Center for Brain-Inspired Computing at Purdue University,
will give a keynote-talk on

"Re-Engineering Computing with Neuro-Inspired Learning: Devices, Circuits and Systems”


and will answer questions afterwards.
Please find the abstract and the biography at the end of the page.

4. Panel discussion: moderated by Josef Broukal

The panel will address the questions set out in the title of the event, namely finding the right balance between "blue sky" basic and "demand driven" applied research, as well as taking the right path between narrow specialization and broad multidisciplinary approach.

We are proud that we could win top-class experts from science and industry:

Mag. Jochen Borenich (COO, Kapsch BusinessCom)
FH-Prof. Christian Kollmitzer (Vice Rector, FH Technikum Wien)
Prof. Kaushik Roy (Director C-BRIC, Purdue University, Indiana, USA)
Prof. Hannes Werthner (Dean of the Faculty of Informatics, TU Wien)

5. Networking with wine & bread in the foyer

Attendance

This event is free and open to the public. However, we kindly ask you to register via this registration form
as this will help us to organize the event. Thank you.

 

Information about our key-note speaker Kaushik Roy

 

 

 

 

Kaushik Roy

 

Biography:
Kaushik Roy is the Edward G. Tiedemann, Jr., Distinguished Professor of Electrical and Computer Engineering at Purdue University. He received his PhD from University of Illinois at Urbana-Champaign in 1990 and joined the Semiconductor Process and Design Center of Texas Instruments, Dallas, where he worked for three years on FPGA architecture development and low-power circuit design. His current research focuses on cognitive algorithms, circuits and architecture for energy-efficient cognitive computing, computing models, and neuromorphic devices. Kaushik has supervised 75 PhD dissertations and his students are well placed in universities and industry. He is the co-author of two books on Low Power CMOS VLSI Design (John Wiley & McGraw Hill).
Kaushik received the National Science Foundation Career Development Award in 1995, IBM faculty partnership award, ATT/Lucent Foundation award, 2005 SRC Technical Excellence Award, SRC Inventors Award, Purdue College of Engineering Research Excellence Award, Humboldt Research Award in 2010, 2010 IEEE Circuits and Systems Society Technical Achievement Award (Charles Doeser Award), Distinguished Alumnus Award from Indian Institute of Technology (IIT), Kharagpur, Global foundries visiting chair at National University of Singapore, Fulbright-Nehru Distinguished Chair, DoD Vannevar Bush Faculty Fellow (2014-2019), Semiconductor Research Corporation Aristotle award in 2015.

Abstract:
"Re-Engineering Computing with Neuro-Inspired Learning: Devices, Circuits and Systems”
Advances in machine learning, notably deep learning, have led to computers matching or surpassing human performance in several cognitive tasks including vision, speech and natural language processing. However, implementation of such neural algorithms in conventional "von-Neumann" architectures are several orders of magnitude more area and power expensive than the biological brain. Hence, we need fundamentally new approaches to sustain exponential growth in performance at high energy-efficiency beyond the end of the CMOS roadmap in the era of ‘data deluge’ and emergent data-centric applications. Exploring the new paradigm of computing necessitates a multi-disciplinary approach: exploration of new learning algorithms inspired from neuroscientific principles, developing network architectures best suited for such algorithms, new hardware techniques to achieve orders of improvement in energy consumption, and nanoscale devices that can closely mimic the neuronal and synaptic operations of the brain leading to a better match between the hardware substrate and the model of computation. In this presentation, we will discuss our work on spintronic device structures consisting of single-domain/domain-wall motion based devices for mimicking neuronal and synaptic units. Implementation of different neural operations with varying degrees of bio-fidelity (from "non-spiking" to "spiking" networks) and implementation of on-chip learning mechanisms (Spike-Timing Dependent Plasticity) will be discussed. Additionally, we also propose probabilistic neural and synaptic computing platforms that can leverage the underlying stochastic device physics of spin-devices due to thermal noise. System-level simulations indicate ~100x improvement in energy consumption for such spintronic implementations over a corresponding CMOS implementation across different computing workloads. Complementary to the above device efforts, we have explored different learning algorithms including stochastic learning with one-bit synapses that greatly reduces the storage/bandwidth requirement while maintaining competitive accuracy, saliency-based attention techniques that scales the computational effort of deep networks for energy-efficiency and adaptive online learning that efficiently utilizes the limited memory and resource constraints to learn new information without catastrophically forgetting already learnt data.

 

The Doctoral College „Resilient Embedded Systems“ (DC-RES) is a cooperation between the
Faculty of Informatics, TU Wien and the FH Technikum Wien.