Efficient Architectures & Sparse Models

Beyond compressing existing models, researchers design architectures that are inherently more efficient. Sparse models are built so that only a fraction of their parameters activate for any given input - unlike dense models where every parameter is involved in every computation. Mixture-of-experts is one example, but sparsity can be applied at many levels. Efficient attention mechanisms reduce the quadratic cost of standard transformer attention - methods like linear attention or sliding window attention approximate the full attention calculation at much lower cost, making it practical to handle longer sequences. Architecture search uses AI to design neural network structures optimised for specific hardware and efficiency constraints. Mobile-optimised architectures like MobileNet and EfficientNet are specifically designed to run on phones and edge devices. The key takeaway is that "the biggest model" and "the best model for your use case" are increasingly different things. Efficiently designed models can deliver excellent performance for specific tasks at a fraction of the cost and latency of general-purpose giants. As the field matures, choosing the right-sized model for your specific needs - rather than defaulting to the largest available - becomes an increasingly important competency.