Neuromorphic technologies adapt biological neural principles to synthesise high-efficiency computational devices, characterised by continuous real-time operation and sparse event-based communication. After several false starts, a confluence of advances now promises widespread commercial adoption. Ultra-low-power Neuromorphic technology will find a home in battery-powered systems, local compute for internet-of-things devices, and consumer wearables. Inspiration from uptake of tensor processors and GPUs can help the field overcome remaining hurdles.

Sadique Sheik and Dylan R Muir. The road to commercial success for neuromorphic technologies. Nature Communications 16:3586 (2025). DOI: 10.1038/s41467-025-57352-1.

Introduction

Two main processing architectures currently dominate commercial computing: von Neumann architectures, comprising the majority of computing devices and now spread like dust in every corner of the planet; and tensor processors such as GPUs and TPUs, which have seen a rapid rise in use for computation since 2010.

Von Neumann highlighted the need to program a device in terms of the fundamental computational functions it can perform [1]. In the case of tensor processors these are matrix multiplications; in the case of CPUs these are primarily basic arithmetic and branching operations. In the case of Neuromorphic (NM) technologies, the basic computational elements are directly inspired by biological nervous systems: temporal integration and dynamics; binary or low-bit-depth multiplication; and thresholding and binary communication between elements.

These technologies comprise a third computational architecture, in addition to CPUs and tensor processors, and several ongoing commercial and research projects are taking steps to bring NM technologies to widespread use. How can we learn from the wild success stories of tensor processors (particularly NVIDIA’s GPUs) and older CPUs, as they rose to dominate the computing landscape? And how can we adapt their success to NM processors?

Since 2010 the rise of deep neural networks has driven and been driven by a concomitant rise in tensor processor architectures, with enormous commercial success in particular for the largest GPU manufacturer NVIDIA. This shift back to parallel processing was directly enabled by the success of deep learning in providing a programming model for tensor computations: the ability to map almost any arbitrary use case to tensor computing, via a data-driven machine-learning approach [2]. Similarly, the commercial success of NVIDIA was driven by their foresight in providing a software Application Programming Interface (API; i.e. CUDA) for their GPUs [3], which allowed this new programming model to be mapped efficiently to their hardware.

In this perspective we outline the early forays of Neuromorphic hardware towards commercial use; describe the new steps the field has made in the last few years; compare and contrast the current design alternatives in nascent commercial Neuromorphic hardware; and sketch a path that we believe will lead to widespread consumer adoption of Neuromorphic technology.

The final straight

There has historically been considerable hand-wringing in the Neuromorphic engineering community about the need for a so-called killer app. This would comprise a groundbreaking application that, standing alone, showcases the superiority of Neuromorphic engineering. An arguably more pertinent query is: Which applications and processors can be enhanced by NM compute?

The IoT and edge sensing market is expanding rapidly, with MEMS pressure sensors alone comprising more than USD$2B in 2023 [4]. NM compute architectures are perfectly placed to enable ultra-low-power sensor-adjacent processing and condition detection for edge devices, and can benefit directly from this growing market. Battery-powered portable systems and consumer wearables in particular can benefit from the low-power edge inference capabilities of commercial NM processors.

In our view, the new example-based, ML-inspired Neuromorphic programming model and associated APIs are themselves the killer apps that will enable commercial uptake of Neuromorphic technologies. These allow almost universal application targeting, permit larger and more complex Neuromorphic applications, and have dramatically accelerated application development for Neuromorphic processors. Coupled with simple inference-only NM processors, the new programming model gives NM technology the look and feel of well-understood systems, while maintaining the significant benefits of the Neuromorphic approach. Taken together, these make NM technology commercially appealing as an alternative computing architecture, for the first time.

The Neuromorphic community has the opportunity to replicate the success of tensor processors, by following a similar path towards commercialisation.

This implies the community should consolidate their efforts around the current crop of open-source tools to avoid further fragmentation. It also means standardisation of benchmarks, and a shift from niche, field-specific benchmarks towards industry-relevant applications where NM processors can be compared directly against commodity hardware. For commercial entities, we suggest the optimal market entry for NM processors will likely be on wearable, edge / IoT and sensor workloads, and recommend to focus product and business development efforts there. Research groups hoping to make commercial impact would do well to address the pain-points and difficulties that have prevented commercialisation until now.

The intriguing combination of neurobiological inspiration for hardware, and engineered optimisation for application building methods, promises to overcome the final hurdles that have held Neuromorphic processors back from widespread commercial success. It is up to the field to capitalise on the convergence of new hardware and new approaches; as in Neuromorphic processors themselves, time (and timing) is a key factor.

References

[1] von Neumann, J. et al. The Computer and the Brain (Yale University Press, 1958).

[2] LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015). https://doi.org/10.1038/nature14539

[3] Nickolls, J., Buck, I., Garland, M. & Skadron, K. Scalable parallel programming with CUDA: Is CUDA the parallel programming model that application developers have been waiting for? Queue 6, 40–53 (2008). https://doi.org/10.1145/1365490.1365500

[4] Clarke, P. Bosch tops two of five pressure sensor rankings. eeNews Europe (2018).

Excerpts and Figure 2 from Sheik & Muir (2025), Nature Communications, published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.