Neuromorphic computing at scale

Area of Science:

• Computer Science
• Neuroscience
• Artificial Intelligence

Background:

• Neuromorphic computing leverages brain-inspired principles for efficient artificial neural network (ANN) hardware and algorithms.
• It is particularly relevant for applications with strict size, weight, and power (SWaP) constraints.
• The field is at a pivotal stage requiring strategic planning for future large-scale development.

Purpose of the Study:

• To outline scalable neuromorphic architectures and identify their key features.
• To discuss applications benefiting from scaled neuromorphic systems and associated challenges.
• To examine the ecosystem required for sustained growth and future opportunities in neuromorphic computing.

Main Methods:

• Reviewing and synthesizing principles from neuroscience and computer science sub-fields.
• Describing approaches for scalable neuromorphic architecture design.
• Analyzing potential applications and implementation challenges for large-scale systems.

Main Results:

• Identification of key features for scalable neuromorphic architectures.
• Discussion of specific applications poised to benefit from system scaling.
• An examination of the necessary ecosystem and emerging opportunities.

Conclusions:

• Strategic guidance is provided for researchers and practitioners in neuromorphic computing.
• The work aims to accelerate the advancement of large-scale neuromorphic systems.
• Future development hinges on scalable architectures, addressing challenges, and fostering a supportive ecosystem.