# Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain

arXiv: https://arxiv.org/abs/1805.08932 · HTML: https://mlsanity.com/paper/1805.08932/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2018.

## Built on by (most cited first, 21 of 21)

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- [Learning through structure: towards deep neuromorphic knowledge graph embeddings](https://mlsanity.com/paper/2109.10376/) (2021)
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- [Deep Phasor Networks: Connecting Conventional and Spiking Neural Networks](https://mlsanity.com/paper/2106.11908/) (2021)
- [An energy-based model for neuro-symbolic reasoning on knowledge graphs](https://mlsanity.com/paper/2110.01639/) (2021)
- [SNE: an Energy-Proportional Digital Accelerator for Sparse Event-Based Convolutions](https://mlsanity.com/paper/2204.10687/) (2022)
- [The role of neuromorphic principles in the future of biomedicine and healthcare](https://mlsanity.com/paper/2603.27716/) (2026)
- [Spike-based Neuromorphic Computing for Next-Generation Computer Vision](https://mlsanity.com/paper/2310.09692/) (2023)
- [A Neuromorphic Approach to Obstacle Avoidance in Robot Manipulation](https://mlsanity.com/paper/2404.05858/) (2024)
- [Neuromorphic Energy-Aware Learning for Adaptive Deep Brain Stimulation](https://mlsanity.com/paper/2606.28600/) (2026)

## Abstract (arXiv)

Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems. The brain has evolved over billions of years to solve difficult engineering problems by using efficient, parallel, low-power computation. The goal of NE is to design systems capable of brain-like computation. Numerous large-scale neuromorphic projects have emerged recently. This interdisciplinary field was listed among the top 10 technology breakthroughs of 2014 by the MIT Technology Review and among the top 10 emerging technologies of 2015 by the World Economic Forum. NE has two-way goals: one, a scientific goal to understand the computational properties of biological neural systems by using models implemented in integrated circuits (ICs); second, an engineering goal to exploit the known properties of biological systems to design and implement efficient devices for engineering applications. Building hardware neural emulators can be extremely useful for simulating large-scale neural models to explain how intelligent behavior arises in the brain. The principle advantages of neuromorphic emulators are that they are highly energy efficient, parallel and distributed, and require a small silicon area. Thus, compared to conventional CPUs, these neuromorphic emulators are beneficial in many engineering applications such as for the porting of deep learning algorithms for various recognitions tasks. In this review article, we describe some of the most significant neuromorphic spiking emulators, compare the different architectures and approaches used by them, illustrate their advantages and drawbacks, and highlight the capabilities that each can deliver to neural modelers.