Neuromorphic Computing for Ultra-Low Power AI: A Spiking Neural Network Accelerator

The energy consumption of deep learning models is a growing concern. This paper presents a novel hardware accelerator for spiking neural networks, a key component of neuromorphic computing, enabling ultra-low power AI inference. Our design achieves significant energy efficiency gains for tasks like pattern recognition and anomaly detection, opening new avenues for always-on AI in edge devices and sustainable computing.