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.
Bibliographical noteFunding Information:
This work was supported by NIH/NIDCD Grant R01 DC014701.
We acknowledge the Sensors Group members and colleagues who have worked on the Dynamic Audio Sensor design and audio systems. Partial funding provided by the Swiss National Science Foundation, HEAR-EAR, 200021172553.
This work was supported by the European project MEMQuD, code 20FUN06. This project (EMPIR 20FUN06 MEMQuD) received funding from the EMPIR programme co-financed by the participating states and from the European Union’s Horizon 2020 research and innovation programme.
This research/project was supported by the Human Brain Project (Grant Agreement Number 785907) of the European Union and a Grant from Intel.
S.R. acknowledges support from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 842492 and a Newcastle University Academic Track (NUAcT) Fellowship.
The design and construction of the SpiNNaker machine was supported by EPSRC (the UK Engineering and Physical Sciences Research Council) under Grants EP/D07908X/1 and EP/G015740/1. Ongoing development of the software is supported by the EU ICT Flagship Human Brain Project (FP7-604102, H2020-720270, H2020-785907, H2020-945539).
This work was supported in part by the European Research Council through the European Union’s Horizon 2020 Research and Innovation Programme under Grant No. 682675.
This work was supported by the National Science Foundation under Grant 1652159 and 1823366.
This work was funded by the European Union under the Horizon 2020 framework programme through the FET-PROACTIVE project HERMES - Hybrid Enhanced Regenerative Medicine Systems, Grant Agreement No. 824164.
This work was partially based on results obtained from a project, JPNP16007, commissioned by the New Energy and Industrial Technology Development Organization (NEDO), and supported in part by JSPS KAKENHI Grant Number 20K11882, JST CREST Grant Number JPMJCR19K2, and JST-Mirai Program Grant Number JPMJMI19B1.
This work was partially supported by the Horizon 2020 European projects MeM-Scales (Grant No. 871371), MNEMOSENE (Grant No. 780215), and NEUROTECH (Grant No. 824103); in part by the Deutsche Forschungsgemeinschaft (SFB 917); in part by the Helmholtz Association Initiative and Networking Fund under Project Number SO-092 (Advanced Computing Architectures, ACA) and in part by the Federal Ministry of Education and Research (BMBF, Germany) in the project NEUROTEC (Project Numbers 16ES1134 and 16ES1133K).
DVC and NP acknowledge the funding from Novo Nordic Foundation Challenge Program for the BioMag project (Grant No. NNF21OC0066526), Villum Fonden, for the NEED project (00027993), Danish Council for Independent Research Technology and Production Sciences for the DFF Research Project 3 (Grant No. 00069B), the European Union’s Horizon 2020, Future and Emerging Technologies (FET) programme (Grant No. 801267) and Danish Council for Independent Research Technology and Production Sciences for the DFF-Research Project 2 (Grant No. 48293). RD acknowledges funding from the German Science foundation within the SFB 917 ‘Nanoswitches’, by the Helmholtz Association Initiative and Networking Fund under Project Number SO-092 (Advanced Computing Architectures, ACA), the Federal Ministry of Education and Research (project NEUROTEC Grant No. 16ES1133K) and the Marie Sklodowska-Curie H2020 European Training Network, ‘Materials for neuromorphic circuits’ (MANIC), grant Agreement No. 861153. BLB acknowledges funding from the European Union’s Horizon 2020 (Grants 824164, 871371, 871501, and 899559). DI acknowledges funding from the European Union’s Horizon 2020 (Grants 824164, 899559 and 101007321).
The research was funded in part by C-BRIC, one of six centres in JUMP, a Semiconductor Research Corporation (SRC) program sponsored by DARPA, the National Science Foundation, the Technology Innovation Institute (Abu Dhabi) and the Amazon Research Award.
© 2022 The Author(s). Published by IOP Publishing Ltd.
- convolutional neural networks
- deep learning
- neuromorphic computation
- self-driving cars
- spiking neural networks
ASJC Scopus subject areas
- Artificial Intelligence
- Hardware and Architecture
- Electrical and Electronic Engineering
- Electronic, Optical and Magnetic Materials