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Neuromorphic Wireless Device-Edge Co-Inference: Efficient Semantic Communication via Directed Information Bottleneck


Core Concepts
A novel hybrid neuromorphic-classical communication and computation architecture for device-edge co-inference that targets energy efficiency at the end device, while leveraging the computational power of the edge, using a directed information bottleneck design criterion.
Abstract
The paper introduces a novel hybrid neuromorphic-classical communication and computation architecture for device-edge co-inference. The key components are: At the transmitter (device), an SNN encoder implemented on neuromorphic hardware performs sensing, processing, and communication using pulse-based modulation. The wireless channel is modeled as a binary symmetric channel, accounting for the joint effects of the channel and demodulation. At the receiver (edge server), a conventional deep learning-based inference network processes the received signal to execute the target semantic task. The system is designed using a directed information bottleneck (DIB) criterion that aims to reduce the communication overhead while retaining the most relevant information for the end-to-end task. A variational formulation (S-VDIB) is presented to enable practical optimization. Numerical results on standard datasets (MNIST-DVS and N-MNIST) demonstrate that the proposed approach outperforms conventional baselines in terms of accuracy and energy efficiency, especially in low SNR regimes. The system also exhibits robustness against a mismatch between training and test SNR conditions. Finally, the paper outlines a preliminary testbed implementation involving a robot wirelessly controlled to mimic the gestures of a user captured via a remote neuromorphic camera.
Stats
The crossover probability ϵ of the binary symmetric channel can be related to the signal-to-noise ratio (SNR) per bit, Eb/N0, as ϵ = Q(2Eb/N0).
Quotes
"The proposed system is designed using an information-theoretic criterion (based on the directed information bottleneck) that targets a reduction of the communication overhead, while retaining the most relevant information for the end-to-end semantic task of interest." "Numerical results on standard data sets validate the proposed architecture, and a preliminary testbed realization is reported."

Deeper Inquiries

How can the proposed neuromorphic wireless device-edge co-inference architecture be extended to support more complex semantic tasks beyond classification, such as segmentation or object detection?

In order to extend the proposed architecture to support more complex semantic tasks like segmentation or object detection, several modifications and enhancements can be implemented: Model Adaptation: The SNN encoder can be modified to handle the specific requirements of segmentation or object detection tasks. For instance, the architecture can be adjusted to output multi-dimensional feature maps instead of classification labels. Incorporating Spatial Information: For tasks like segmentation or object detection, spatial information is crucial. The SNN encoder can be designed to preserve spatial relationships in the input data, enabling the decoder to make more informed decisions. Hierarchical Processing: Implementing a hierarchical processing approach where different layers of the SNN encoder capture features at different levels of abstraction can enhance the system's ability to perform complex tasks. Integration of Attention Mechanisms: Including attention mechanisms in the SNN encoder can improve the focus on relevant parts of the input data, aiding in tasks that require detailed analysis like object detection. Training Data Augmentation: To support more diverse and complex tasks, augmenting the training data with variations in lighting conditions, backgrounds, and object orientations can help the system generalize better. By incorporating these enhancements and adjustments, the neuromorphic wireless device-edge co-inference architecture can be extended to effectively handle more complex semantic tasks beyond simple classification.

What are the potential challenges and trade-offs in jointly optimizing the neuromorphic encoder and the conventional deep learning-based decoder for end-to-end performance, rather than the current transmitter-centric design?

Optimizing the neuromorphic encoder and the conventional deep learning-based decoder jointly for end-to-end performance presents several challenges and trade-offs: Complexity: Integrating the optimization of both components requires a more intricate design and training process, potentially increasing the overall complexity of the system. Training Synchronization: Ensuring that the encoder and decoder are trained synchronously and effectively communicate with each other during the training process can be challenging. Hyperparameter Tuning: Balancing the hyperparameters of the encoder and decoder to achieve optimal end-to-end performance may require extensive tuning and experimentation. Resource Allocation: Allocating computational resources between the encoder and decoder to maximize overall system efficiency while meeting performance requirements can be a delicate balance. Latency: Coordinating the processing between the encoder and decoder in real-time applications may introduce latency, impacting the system's responsiveness. Interoperability: Ensuring seamless interoperability between the neuromorphic encoder and the conventional decoder, especially when they are based on different architectures, can be a significant challenge. By addressing these challenges and carefully managing the trade-offs between optimization complexity, resource allocation, and system performance, a jointly optimized neuromorphic encoder and conventional deep learning-based decoder can achieve superior end-to-end performance.

Given the event-driven nature of the neuromorphic sensor and processing, how can the system be further optimized to leverage the temporal dynamics of the input data for improved energy efficiency and inference accuracy?

To optimize the system further and leverage the temporal dynamics of the input data for enhanced energy efficiency and inference accuracy, the following strategies can be implemented: Temporal Feature Extraction: Design the SNN encoder to extract temporal features efficiently from the event-driven input data, capturing the temporal dynamics of the signals for more accurate inference. Dynamic Spike Rate Adjustment: Implement mechanisms to dynamically adjust the spike rates based on the temporal characteristics of the input data, optimizing energy consumption while maintaining inference accuracy. Event-Based Processing: Utilize event-based processing techniques to focus computational resources on relevant temporal events, reducing unnecessary computations and conserving energy. Spiking Neural Network Optimization: Fine-tune the parameters of the SNN encoder to effectively capture and process the temporal dynamics of the input data, enhancing the system's ability to extract meaningful information. Real-Time Adaptation: Enable the system to adapt in real-time to changing temporal patterns in the input data, allowing for dynamic adjustments in processing to improve inference accuracy and energy efficiency. Feedback Mechanisms: Implement feedback mechanisms that utilize temporal information to provide input for adjusting the processing parameters, optimizing the system's performance based on the temporal dynamics of the data. By incorporating these optimization strategies, the system can effectively leverage the temporal dynamics of the input data, leading to improved energy efficiency and inference accuracy in event-driven neuromorphic processing.
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