Energy Efficient Neural Network

Energy-efficient neural networks aim to reduce the substantial computational and energy demands of deep learning, addressing the growing concern of "Red-AI." Current research focuses on alternative architectures like spiking neural networks (SNNs) and the exploration of novel hardware, including memristors and photonic devices, to accelerate computations and lower power consumption. These efforts involve optimizing network architectures through techniques such as sparsity, quantization, and specialized algorithms, alongside developing new training methods and hardware-software co-design approaches. The ultimate goal is to enable the deployment of powerful AI models on resource-constrained devices and reduce the environmental impact of AI.