Reimagining Edge AI and LLM Inference with Compute Memory Architectures
Abstract Recent advances in artificial intelligence (AI), especially in large language models (LLMs), have dramatically increased model sizes and computational demands, significantly straining computing system capabilities. This issue is particularly acute in resource-constrained edge AI scenarios, where efficient hardware acceleration of compute-intensive tasks and optimization of data reuse to minimize costly data transfers are essential. […]
