IOP Publishing Journal: Neuromorphic Computing and Engineering 2021 Roadmap on Neuromorphic Computing and Engineering Dennis V. Christensen1, Regina Dittmann2, Bernabé Linares-Barranco3, Abu Sebastian4, Manuel Le Gallo4, Andrea Redaelli5, Stefan Slesazeck6, Thomas Mikolajick6,7, Sabina Spiga8, Stephan Menzel9, Ilia Valov9, Gianluca Milano10, Carlo Ricciardi11, Shi-Jun Liang12, Feng Miao12, Mario Lanza13, Tyler J. Quill14, Scott T. Keene15, Alberto Salleo14, Julie Grollier16, Danijela Marković16, Alice Mizrahi16, Peng Yao17, J. Joshua Yang17, Giacomo Indiveri18, John Paul Strachan19, Suman Datta20, Elisa Vianello21, Alexandre Valentian22, Johannes Feldmann23, Xuan Li23, Wolfram H.P. Pernice24,25, Harish Bhaskaran23, Emre Neftci26, Srikanth Ramaswamy27, Jonathan Tapson28, Franz Scherr29, Wolfgang Maass29, Priyadarshini Panda30, Youngeun Kim30, Gouhei Tanaka31, Simon Thorpe32, Chiara Bartolozzi33, Thomas A. Cleland34, Christoph Posch35, Shih-Chii Liu18, Arnab Neelim Mazumder36, Morteza Hosseini36, Tinoosh Mohsenin36, Elisa Donati18, Silvia Tolu37, Roberto Galeazzi37, Martin Ejsing Christensen38, Sune Holm39, Daniele Ielmini40, and N. Pryds1,41. 1 Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark 2 Peter Gruenberg Institute 7, Forschungszentrum Juelich GmbH, 52425 Juelich, Germany and JARA-FIT, RWTH Aachen University, 52056 Aachen, Germany 3 Instituto de Microelectrónica de Sevilla (IMSE-CNM), CSIC and Universidad de Sevilla, 41092 Seville, Spain 4 IBM Research – Zurich, Switzerland 5 STMicroelectronics, Agrate, Italy 6 NaMLab gGmbH, 01187 Dresden, Germany 7 Institute of Semiconductors and Microsystems, TU Dresden; Dresden, Germany 8 CNR-IMM, Agrate Brianza (MB), Italy 9 FZ Juelich (PGI-7), Juelich , Germany 10 Advanced Materials Metrology and Life Science Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Torino, Italy 11 Department of Applied Science and Technology, Politecnico di Torino, Torino, Italy 12 National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, China 13 Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Saudi Arabia. 14 Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, United States of America 15 Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom. 16 Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767 Palaiseau, France 17 Electrical and Computer Engineering Department, University of Southern California, Los Angeles, CA, USA 18 Institute of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland 19 Hewlett Packard Laboratories, Hewlett Packard Enterprise, San Jose, CA, USA 20 Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN, USA 21 CEA, LETI, Université Grenoble Alpes, Grenoble, France 22 CEA, LIST, Université Grenoble Alpes, Grenoble, France 23 Department of Materials, University of Oxford, Parks Road, OX1 3PH Oxford, UK 24 Institute of Physics, University of Münster, Heisenbergstr. 11, 48149 Münster, Germany Heisenbergstr. 25 Center for Soft Nanoscience, University of Münster, 48149 Münster, Germany 26 Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, USA 27 École Polytechnique Fédérale de Lausanne, Geneva, Switzerland 28 School of Electrical and Data Engineering, University of Technology, Sydney, Australia 29 Institute of Theoretical Computer Science, Graz University of Technology, Graz, Austria 30 Department of Electrical Engineering, New Haven, Yale University, USA 31 International Research Center for Neurointelligence (IRCN), The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo 113-0033, Japan 32 CerCo, Université Toulouse 3, CNRS, CHU Purpan, Pavillon Baudot, 31059 Toulouse, France 33 Event Driven Perception for Robotics, Italian Institute of Technology, iCub Facility, Genoa, Italy 34 Dept. of Psychology, Cornell University, Ithaca, NY, USA 35 Prophesee, Paris, France 36 University of Maryland, Baltimore County, Catonsville, USA 37 Technical University of Denmark, Denmark 38 The Danish Council on Ethics, Denmark 39 Department of Food and Resource Economics, University of Copenhagen, Denmark 40 Politecnico di Milano, 20133 Milano, Italy 41 Email: Nini Pryds, [email protected] Abstract: Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In this 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 — that is a quadrillion (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 and unstructured data as our brain does. Neuromorphic computing systems are aimed at addressing these needs. The human brain performs about 1015 calculations per second using 20W and a 1.2L volume. By taking inspiration from biology, new generation computers could have much lower power consumption than conventional processors, could exploit integrated non-volatile memory and logic, and could be explicitly designed to support dynamic learning in the context of complex and unstructured data. Among their potential future applications, business, health care, social security, disease and viruses spreading control might be the most impactful at societal level. This roadmap envisages the potential applications of neuromorphic materials in cutting edge technologies and focuses on the design and fabrication of artificial neural systems. The contents of this roadmap will highlight the interdisciplinary nature of this activity which takes inspiration from biology, physics, mathematics, computer science and engineering and will target new computational substrates and engines, based on new materials and engineering principles for efficient and low-power neuromorphic computing; new architectures and algorithms for learning and adaptation/plasticity. This will provide a roadmap to explore and consolidate new technology behind both present and future applications in many technologically relevant areas. Contents: Introduction Section 1 - Materials: 1.1 – Phase-change memory 1.2 – Ferroelectric devices 1.3 – Valence change memory 1.4 – Electrochemical metallization cells 1.5 – Nanowire networks 1.6 – 2D materials 1.7 – Organic materials 1.8 – Spintronics Section 2 – Neuromorphic circuits: 2.1 – Deep learning 2.2 – Spiking neural networks 2.3 – Emerging hardware approaches for optimization 2.4 – Enabling technologies for the future heterogeneous neuromorphic accelerators 2.5 – Photonics Section 3 – Neuromorphic Algorithms: 3.1 – Learning in spiking neural networks 3.2 – Computational neuroscience 3.3 – Stochastic Computing 3.4 – Learning to learn in neuromorphic hardware 3.5 – Convolutional neural networks 3.6 – Reservoir computing 3.7 – Computing with spikes Section 4 – Applications: 4.1 – Robotics 4.2 – Self-driving cars 4.3 – Neuromorphic computing for olfaction and chemosensation 4.4 – Event vision sensors 4.5 – Neuromorphic audition 4.6 – Assessment of Multivariate Time-Series Signals for Low Power Embedded Applications – A Hardware Perspective 4.7 – Wearable Electromyography Processing 4.8 – Collaborative Autonomous System Section 5 – Ethics: 5.1 – The ethics of developing neuromorphic technology Introduction Computers have become essential to all aspects of modern life and are omnipresent all over the globe. Today, the recent data-intensive applications have placed a high demand on hardware performance, e.g. access latency, capacity, bandwidth, and cost, and on the cognitive level (AI), e.g., data processing modes, architectures, and algorithms. However, the ever-growing pressure for big data creates additional challenges: (1) energy consumption - due to the rapid development of sophisticated algorithms and architectures, power dissipation has become a big challenge and currently, about 5–15% of the world’s energy is spent in some form of data manipulation (transmission or processing); (2) memory bandwidth - Von-Neumann’s computer architectures have made an incredible contribution to the world of science and technology for decades, however, their performance is still inefficient due to the need to constantly shuffle data between physically separated logic and memory units. The current progress in Complementary Metal Oxide Semiconductor (CMOS) technology and computer hardware will not be self-sustained forever. Furthermore, these computers will not have the intrinsic capabilities to learn or deal with complex and unstructured data as the human brain does. Thus, there are significant research efforts worldwide in developing a profoundly different approach inspired by biological principles - neuromorphic systems. Inspiration from nature is a vital part of our advancement to a more sustainable future with neuromorphic devices having much lower power consumption than conventional processors, integrated non-volatile memory and logic, and dynamic learning capabilities in the context of complex and unstructured data. Building artificial neural