Beyond Transformers: Emerging Neural Architectures, Energy-Efficient AI, and Multimodal Fusion for Next-Generation Artificial Intelligence

It analyzes the limitations of traditional Transformer models (such as quadratic computational complexity and high energy consumption) and explores promising alternative architectures like State Space Models (SSMs) and Mamba. The paper also investigates techniques for energy-efficient AI (including quantization, pruning, knowledge distillation, and neuromorphic computing) alongside methods for multimodal fusion—integrating text, image, audio, and sensor data into unified AI systems. Overall, it argues that the future of AI lies in developing scalable, sustainable, and efficient models that move beyond a single dominant architecture