Spiking Neural Networks (SNN)
What is Spiking Neural Networks?
Spiking Neural Networks (SNNs) are a type of artificial neural network that mimic the way biological neurons communicate through discrete spikes. In Edge AI, they enable ultra-efficient, real-time data processing on devices, making them ideal for IoT sensors, wearables, and low-power edge applications.
Spiking Neural Networks are also neural models where information is transmitted as discrete electrical spikes rather than continuous signals. This mimics human brain activity, allowing faster and more energy-efficient computation at the edge. SNNs are also called “neuromorphic networks.”
Why Is It Used?
SNNs are used to process data locally on edge devices with minimal latency and energy consumption. They are particularly valuable in scenarios requiring real-time decision-making, such as autonomous vehicles, smart cameras, and industrial IoT systems.
How Is It Used?
Event-based sensing: Devices trigger computations only when data changes, reducing unnecessary processing.
Edge inference: SNNs analyze sensor data locally without sending it to the cloud.
Energy optimization: Spiking activity ensures low-power operations for battery-constrained devices.
Types of Spiking Neural Networks
Feedforward SNNs: Process input spikes in one direction for tasks like pattern recognition.
Recurrent SNNs: Incorporate feedback loops for temporal data and sequence learning.
Convolutional SNNs: Adapt CNN architectures for event-driven visual processing.
Benefits of Spiking Neural Networks
Ultra-low power consumption for battery-dependent edge devices.
Real-time processing with minimal latency.
Scalable and adaptive for dynamic environments and streaming data.
Enhanced accuracy in temporal and event-driven tasks compared to traditional neural networks.