2022 Roadmap on Neuromorphic Computing and Engineering (2105.05956v3)

Abstract: Modern computation based on the von Neumann architecture is today 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. We hope that this Roadmap will be a useful resource to readers outside this field, for those who are just entering the field, and for those who are well established in the neuromorphic community. https://doi.org/10.1088/2634-4386/ac4a83

• The paper presents a strategic roadmap that identifies key challenges and proposes solutions for energy-efficient neuromorphic computing architectures.
• It details the integration of memory and processing through brain-inspired designs and novel devices like memristors to enhance performance.
• The roadmap underscores promising applications in edge computing and adaptive AI while urging ethical system design.

Neuromorphic Computing: Challenges and Roadmap

Neuromorphic computing represents a promising frontier within the computational landscape, aiming to emulate the efficiency and functionality of biological systems, particularly the human brain. The "2022 Roadmap on Neuromorphic Computing and Engineering" offers a comprehensive overview of the current state and future potential of this field. The document is an interdisciplinary collaboration that leverages perspectives from leading researchers, thereby laying the groundwork for future developments in neuromorphic systems.

State of the Field

Neuromorphic computing technologies seek to transcend the limitations inherent in the von Neumann architecture, primarily the energy and latency constraints associated with data transfer between separate processing and memory units. By designing systems inspired by neural models, neuromorphic computing offers a paradigm that integrates memory and processing, thus promising significant gains in power efficiency. The use of memristors, numerous other emergent materials, and novel device architectures holds the potential to revolutionize the way computing handles power and scalability challenges.

Challenges and Directions

Despite its promise, several challenges must be addressed for neuromorphic computing to reach its full potential. A primary barrier lies in the variability and non-ideal behavior of emerging memristive technologies, which can impact computational accuracy and reliability. The roadmap emphasizes the necessity of improving materials science to minimize this variability and enhance device uniformity.

Moreover, the complexity of integrating neuromorphic devices with existing CMOS technologies cannot be overstated. This requires robust back-end processes that do not impede device functionality. Power consumption, particularly from periphery circuits such as analog-to-digital converters (ADCs), is another bottleneck. To this end, efficient conversion and signal-processing schemes must be implemented.

Future Potential and Applications

The roadmap points to promising applications in edge computing, where the low-power, high-efficiency nature of neuromorphic systems can be leveraged for real-time data processing in environments with constrained energy resources. This offers substantial advantages in mobile and embedded devices, expanding their capabilities while extending operational lifetimes. The potential for neuromorphic systems in simulating biological processes also holds promise for advancements in machine learning and artificial intelligence, particularly in domains requiring sensory integration and adaptive learning.

Ethical and Societal Considerations

Equally important to technical advancements are the ethical questions raised by neuromorphic technologies. As systems grow more autonomous and capable, ensuring transparency and ethical considerations becomes imperative. The roadmap suggests integrating ethics from the ground up in system design, acknowledging the broader societal impacts of these technologies.

Conclusion

The "2022 Roadmap on Neuromorphic Computing and Engineering" presents a detailed exploration of the challenges and upcoming milestones in neuromorphic computing. While technical hurdles remain, the roadmap provides a strategic framework for advancing this field, heralding a future where computing systems can emulate the adaptive, low-power characteristics of biological brains. This synthesis of multidisciplinary insights underscores the integrative approach necessary to transform neuromorphic computing from a theoretical concept to a practical, transformative technology in modern computing.