# Low-Power Neuromorphic Hardware for Signal Processing Applications

arXiv: https://arxiv.org/abs/1901.03690 · HTML: https://mlsanity.com/paper/1901.03690/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2019.

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

- [Federated Learning with Spiking Neural Networks](https://mlsanity.com/paper/2106.06579/) (2021)
- [Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference](https://mlsanity.com/paper/2206.06047/) (2022)
- [Spiking-GAN: A Spiking Generative Adversarial Network Using Time-To-First-Spike Coding](https://mlsanity.com/paper/2106.15420/) (2021)
- [A New Look at Spike-Timing-Dependent Plasticity Networks for Spatio-Temporal Feature Learning](https://mlsanity.com/paper/2111.00791/) (2021)
- [Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity](https://mlsanity.com/paper/2302.01302/) (2023)
- [Improvement of Spiking Neural Network with Bit Planes and Color Models](https://mlsanity.com/paper/2410.08229/) (2024)
- [Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks](https://mlsanity.com/paper/2206.12520/) (2022)
- [Modern Neuromorphic AI: From Intra-Token to Inter-Token Processing](https://mlsanity.com/paper/2601.00245/) (2026)
- [Including STDP to eligibility propagation in multi-layer recurrent spiking neural networks](https://mlsanity.com/paper/2201.07602/) (2022)
- [A Convolutional Spiking Network for Gesture Recognition in Brain-Computer Interfaces](https://mlsanity.com/paper/2304.11106/) (2023)
- [Position: Olfaction Standardization is Essential for the Advancement of Embodied Artificial Intelligence](https://mlsanity.com/paper/2506.00398/) (2025)
- [Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning](https://mlsanity.com/paper/2506.21324/) (2025)
- [Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback](https://mlsanity.com/paper/2508.06292/) (2025)
- [Hybrid Layer-Wise ANN-SNN With Surrogate Spike Encoding-Decoding Structure](https://mlsanity.com/paper/2509.24411/) (2025)
- [Spatiotemporal Pattern Recognition in Single Mixed-Signal VLSI Neurons with Heterogeneous Dynamic Synapses](https://mlsanity.com/paper/2106.05686/) (2021)
- [Time Encoding Quantization of Bandlimited and Finite-Rate-of-Innovation Signals](https://mlsanity.com/paper/2110.01928/) (2021)
- [Energy-Efficient Visual Search by Eye Movement and Low-Latency Spiking Neural Network](https://mlsanity.com/paper/2310.06578/) (2023)
- [A Neuromorphic Approach to Obstacle Avoidance in Robot Manipulation](https://mlsanity.com/paper/2404.05858/) (2024)
- [Neuromorphic-based metaheuristics: A new generation of low power, low latency and small footprint optimization algorithms](https://mlsanity.com/paper/2505.16362/) (2025)
- [Hardware-Aware Fine-Tuning of Spiking Q-Networks on the SpiNNaker2 Neuromorphic Platform](https://mlsanity.com/paper/2507.23562/) (2025)
- [Federated Learning of Spiking Neural Networks under Heterogeneous Temporal Resolutions](https://mlsanity.com/paper/2605.15355/) (2026)

## Abstract (arXiv)

Machine learning has emerged as the dominant tool for implementing complex cognitive tasks that require supervised, unsupervised, and reinforcement learning. While the resulting machines have demonstrated in some cases even super-human performance, their energy consumption has often proved to be prohibitive in the absence of costly super-computers. Most state-of-the-art machine learning solutions are based on memory-less models of neurons. This is unlike the neurons in the human brain, which encode and process information using temporal information in spike events. The different computing principles underlying biological neurons and how they combine together to efficiently process information is believed to be a key factor behind their superior efficiency compared to current machine learning systems. Inspired by the time-encoding mechanism used by the brain, third generation spiking neural networks (SNNs) are being studied for building a new class of information processing engines. Modern computing systems based on the von Neumann architecture, however, are ill-suited for efficiently implementing SNNs, since their performance is limited by the need to constantly shuttle data between physically separated logic and memory units. Hence, novel computational architectures that address the von Neumann bottleneck are necessary in order to build systems that can implement SNNs with low energy budgets. In this paper, we review some of the architectural and system level design aspects involved in developing a new class of brain-inspired information processing engines that mimic the time-based information encoding and processing aspects of the brain.