Thomas Mikolajick

Prof. Dr. Thomas Mikolajick

orcid

Research Focus & Metrics

Academic Career Length

34Years (1993-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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23 July 2012 in Nano Letters
Verified
Journal Article
Closed access
JMJohannes Müller
Johannes Müller
TBTim S. Böscke
Tim S. Böscke
USUwe Schröder
Uwe Schröder
SMStefan Mueller
Stefan Mueller
DBDennis Bräuhaus
Dennis Bräuhaus
TMThomas Mikolajick
Thomas Mikolajick

11 February 2015 in Advanced Materials
Verified
Journal Article
Closed access
PDF file
MPMin Hyuk Park
Min Hyuk Park
YLYoung Hwan Lee
Young Hwan Lee
HKHan Joon Kim
Han Joon Kim
YKYu Jin Kim
Yu Jin Kim
TMTaehwan Moon
Taehwan Moon
CHCheol Seong Hwang
Cheol Seong Hwang

6 May 2016 in Advanced Functional Materials
Verified
Journal Article
Closed access
PDF file
MPMilan Pešić
Milan Pešić
FFFranz Paul Gustav Fengler
Franz Paul Gustav Fengler
LLLuca Larcher
Luca Larcher
APAndrea Padovani
Andrea Padovani
TSTony Schenk
Tony Schenk
TMThomas Mikolajick
Thomas Mikolajick

15 March 2012 in Advanced Functional Materials
Verified
Journal Article
Closed access
PDF file
SMStefan Mueller
Stefan Mueller
JMJohannes Mueller
Johannes Mueller
ASAarti Singh
Aarti Singh
SRStefan Riedel
Stefan Riedel
JSJonas Sundqvist
Jonas Sundqvist
TMThomas Mikolajick
Thomas Mikolajick

20 May 2022 in Neuromorphic Computing and Engineering
Verified
Journal Article
Open access
DCDennis V Christensen
Dennis V Christensen
RDRegina Dittmann
Regina Dittmann
BLBernabe Linares-Barranco
Bernabe Linares-Barranco
ASAbu Sebastian
Abu Sebastian
MGManuel Le Gallo
Manuel Le Gallo
NPN Pryds
N Pryds
Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community.

30 March 2022 in Nature Reviews Materials
Verified
Journal Article
Closed access
PDF file
USUwe Schroeder
Uwe Schroeder
MPMin Hyuk Park
Min Hyuk Park
TMThomas Mikolajick
Thomas Mikolajick
CHCheol Seong Hwang
Cheol Seong Hwang