Neuromorphic Wireless Split Computing with Multi-Level Spikes

• URL: http://arxiv.org/abs/2411.04728v2
• Date: Mon, 03 Feb 2025 15:51:59 GMT
• Title: Neuromorphic Wireless Split Computing with Multi-Level Spikes
• Authors: Dengyu Wu, Jiechen Chen, Bipin Rajendran, H. Vincent Poor, Osvaldo Simeone,
• Abstract summary: Neuromorphic computing uses spiking neural networks (SNNs) to perform inference tasks. embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption. split computing - where an SNN is partitioned across two devices - is a promising solution. This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs.
• Score: 69.73249913506042
• License:
• Abstract: Inspired by biological processes, neuromorphic computing leverages spiking neural networks (SNNs) to perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have shown that embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption. To scale neuromorphic computing to larger workloads, split computing - where an SNN is partitioned across two devices - is a promising solution. In such architectures, the device hosting the initial layers must transmit information about the spikes generated by its output neurons to the second device. This establishes a trade-off between the benefits of multi-level spikes, which carry additional payload information, and the communication resources required for transmitting extra bits between devices. This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs. We propose digital and analog modulation schemes for an orthogonal frequency division multiplexing (OFDM) radio interface to enable efficient communication. Simulation and experimental results using software-defined radios reveal performance improvements achieved by multi-level SNN models and provide insights into the optimal payload size as a function of the connection quality between the transmitter and receiver.

Related papers

• Deep-Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware [78.17783007774295]
  This paper explores the potential of conversion-based neuromorphic algorithms for highly accurate and energy-efficient single-snapshot multidimensional harmonic retrieval. A novel method for converting the complex-valued convolutional layers and activations into spiking neural networks (SNNs) is developed. The converted SNNs achieve almost five-fold power efficiency at moderate performance loss compared to the original CNNs.
  arXiv  Detail & Related papers  (2024-12-05T09:41:33Z)
• Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning [97.99077847606624]
  This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs. A key challenge in the design of a wake-up radio-based neuromorphic split computing system is the selection of thresholds for sensing, wake-up signal detection, and decision making.
  arXiv  Detail & Related papers  (2024-04-02T10:19:04Z)
• Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing [59.40731173370976]
  We investigate the application of energy-efficient brain-inspired machine learning models for on-board radio resource management. For relevant workloads, spiking neural networks (SNNs) implemented on Loihi 2 yield higher accuracy, while reducing power consumption by more than 100$times$ as compared to the CNN-based reference platform.
  arXiv  Detail & Related papers  (2023-08-22T03:13:57Z)
• Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference [32.0035037154674]
  This paper proposes an end-to-end design for a neuromorphic wireless Internet-of-Things system. Each sensing device is equipped with a neuromorphic sensor, a spiking neural network (SNN), and an impulse radio transmitter with multiple antennas. Pilots, encoding SNNs, decoding SNN, and hypernetwork are jointly trained across multiple channel realizations.
  arXiv  Detail & Related papers  (2022-06-13T11:13:39Z)
• Deep Convolutional Learning-Aided Detector for Generalized Frequency Division Multiplexing with Index Modulation [0.0]
  The proposed method first pre-processes the received signal by using a zero-forcing (ZF) detector and then uses a neural network consisting of a convolutional neural network (CNN) followed by a fully-connected neural network (FCNN) The FCNN part uses only two fully-connected layers, which can be adapted to yield a trade-off between complexity and bit error rate (BER) performance. It has been demonstrated that the proposed deep convolutional neural network-based detection and demodulation scheme provides better BER performance compared to ZF detector with a reasonable complexity increase.
  arXiv  Detail & Related papers  (2022-02-06T22:18:42Z)
• Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision [64.71260357476602]
  Event-based vision sensors encode local pixel-wise brightness changes in streams of events rather than image frames. Recent progress in object recognition from event-based sensors has come from conversions of deep neural networks. We propose a hybrid architecture for end-to-end training of deep neural networks for event-based pattern recognition and object detection.
  arXiv  Detail & Related papers  (2021-12-06T23:45:58Z)
• Spiking Neural Networks -- Part III: Neuromorphic Communications [38.518936229794214]
  The presence of more and more wirelessly connected devices is driving efforts to export advances in machine learning. Implementing machine learning models for learning and inference on battery-powered devices that are connected via bandwidth-constrained channels remains challenging. This paper explores two ways in which Spiking Neural Networks (SNNs) can help address these open problems.
  arXiv  Detail & Related papers  (2020-10-27T11:52:35Z)
• Inference with Artificial Neural Networks on Analog Neuromorphic Hardware [0.0]
  BrainScaleS-2 ASIC comprises mixed-signal neurons and synapse circuits. System can also operate in a vector-matrix multiplication and accumulation mode for artificial neural networks.
  arXiv  Detail & Related papers  (2020-06-23T17:25:06Z)
• Flexible Transmitter Network [84.90891046882213]
  Current neural networks are mostly built upon the MP model, which usually formulates the neuron as executing an activation function on the real-valued weighted aggregation of signals received from other neurons. We propose the Flexible Transmitter (FT) model, a novel bio-plausible neuron model with flexible synaptic plasticity. We present the Flexible Transmitter Network (FTNet), which is built on the most common fully-connected feed-forward architecture.
  arXiv  Detail & Related papers  (2020-04-08T06:55:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.