# Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference arXiv: https://arxiv.org/abs/2206.06047 ยท HTML: https://mlsanity.com/paper/2206.06047/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Builds on 22 of its 47 references that are in the map. - First posted on arXiv 13 Jun 2022. ## Builds on (most cited first, 22 of 22) - [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017) - [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016) - [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019) - [Events-to-Video: Bringing Modern Computer Vision to Event Cameras](https://mlsanity.com/paper/1904.08298/) (2019) - [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020) - [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021) - [Semantics-Empowered Communication for Networked Intelligent Systems](https://mlsanity.com/paper/2007.11579/) (2020) - [Brain-inspired computing: We need a master plan](https://mlsanity.com/paper/2104.14517/) (2021) - [Bandwidth-Agile Image Transmission with Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2009.12480/) (2020) - [Deep Joint Source-Channel Coding for Wireless Image Transmission](https://mlsanity.com/paper/1809.01733/) (2018) - [XNOR Neural Engine: a Hardware Accelerator IP for 21.6 fJ/op Binary Neural Network Inference](https://mlsanity.com/paper/1807.03010/) (2018) - [An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications](https://mlsanity.com/paper/1910.01059/) (2019) - [Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM](https://mlsanity.com/paper/2101.03909/) (2021) - [Low-Power Neuromorphic Hardware for Signal Processing Applications](https://mlsanity.com/paper/1901.03690/) (2019) - [Semantic-Effectiveness Filtering and Control for Post-5G Wireless Connectivity](https://mlsanity.com/paper/1907.02441/) (2019) - [Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck](https://mlsanity.com/paper/2106.01177/) (2021) - [TeraHertz Band Communication: An Old Problem Revisited and Research Directions for the Next Decade](https://mlsanity.com/paper/2112.13187/) (2021) - [Towards Goal-Oriented Semantic Signal Processing: Applications and Future Challenges](https://mlsanity.com/paper/2109.11885/) (2021) - [Deep HyperNetwork-Based MIMO Detection](https://mlsanity.com/paper/2002.02750/) (2020) - [End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligence](https://mlsanity.com/paper/2009.01527/) (2020) - [Spiking Neural Networks -- Part II: Detecting Spatio-Temporal Patterns](https://mlsanity.com/paper/2010.14217/) (2020) - [HyperRNN: Deep Learning-Aided Downlink CSI Acquisition via Partial Channel Reciprocity for FDD Massive MIMO](https://mlsanity.com/paper/2104.12274/) (2021) ## Built on by (most cited first, 16 of 16) - [Semantics-Empowered Communication: A Tutorial-cum-Survey](https://mlsanity.com/paper/2212.08487/) (2022) - [Knowing When to Stop: Delay-Adaptive Spiking Neural Network Classifiers with Reliability Guarantees](https://mlsanity.com/paper/2305.11322/) (2023) - [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024) - [Task-Oriented Integrated Sensing, Computation and Communication for Wireless Edge AI](https://mlsanity.com/paper/2306.06603/) (2023) - [Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities](https://mlsanity.com/paper/2307.06687/) (2023) - [Neuromorphic Wireless Split Computing with Multi-Level Spikes](https://mlsanity.com/paper/2411.04728/) (2024) - [Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing](https://mlsanity.com/paper/2308.11152/) (2023) - [Neuromorphic Event-Driven Semantic Communication in Microgrids](https://mlsanity.com/paper/2402.18390/) (2024) - [Modern Neuromorphic AI: From Intra-Token to Inter-Token Processing](https://mlsanity.com/paper/2601.00245/) (2026) - [A Convolutional Spiking Network for Gesture Recognition in Brain-Computer Interfaces](https://mlsanity.com/paper/2304.11106/) (2023) - [Neuromorphic Wireless Device-Edge Co-Inference via the Directed Information Bottleneck](https://mlsanity.com/paper/2404.01804/) (2024) - [Inferring Ingrained Remote Information in AC Power Flows Using Neuromorphic Modality Regime](https://mlsanity.com/paper/2407.14883/) (2024) - [Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning](https://mlsanity.com/paper/2506.21324/) (2025) - [Quantization-Aware Collaborative Inference for Large Embodied AI Models](https://mlsanity.com/paper/2602.13052/) (2026) - [Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons](https://mlsanity.com/paper/2506.20015/) (2025) - [Online Reliable Anomaly Detection via Neuromorphic Sensing and Communications](https://mlsanity.com/paper/2510.14688/) (2025) ## Abstract (arXiv) Neuromorphic computing is an emerging computing paradigm that moves away from batched processing towards the online, event-driven, processing of streaming data. Neuromorphic chips, when coupled with spike-based sensors, can inherently adapt to the "semantics" of the data distribution by consuming energy only when relevant events are recorded in the timing of spikes and by proving a low-latency response to changing conditions in the environment. This paper proposes an end-to-end design for a neuromorphic wireless Internet-of-Things system that integrates spike-based sensing, processing, and communication. In the proposed NeuroComm system, each sensing device is equipped with a neuromorphic sensor, a spiking neural network (SNN), and an impulse radio transmitter with multiple antennas. Transmission takes place over a shared fading channel to a receiver equipped with a multi-antenna impulse radio receiver and with an SNN. In order to enable adaptation of the receiver to the fading channel conditions, we introduce a hypernetwork to control the weights of the decoding SNN using pilots. Pilots, encoding SNNs, decoding SNN, and hypernetwork are jointly trained across multiple channel realizations. The proposed system is shown to significantly improve over conventional frame-based digital solutions, as well as over alternative non-adaptive training methods, in terms of time-to-accuracy and energy consumption metrics.