LLM-NPU: Towards Efficient Foundation Model Inference on Low-Power Neural Processing Units

Neural processing units (NPUs) have become essential in modern client and edge platforms, offering unparalleled efficiency by delivering high throughput at low power. This is critical to improve the TOPS/W of the NPU, leading to longer battery life. While NPUs were initially designed to efficiently execute computer vision (CV) workloads such as CNNs, the rising demand to run transformer-based large language models (LLMs) locally now calls for significant architectural and software adaptation. This paper presents LLM-NPU, a comprehensive software-hardware co-optimization framework that enables scalable, power-efficient LLM deployment on NPUs under tight compute and memory budgets. We present software solutions such as vertical and horizontal operator fusion, quantization-aware weight compression, hybrid key-value (KV) quantization, eviction strategies, and static-shape inference that target memory bottlenecks and compute inefficiencies in LLM execution. On the hardware side, we explore domain-specialized NPU enhancements, including processing-in-memory architectures, extended input channel accumulation, structured sparsity acceleration, GEMM engine optimizations, mixed precision, microscaling format support, and fusion-aware execution pipelines. These co-designed innovations can collectively improve the energy, throughput, and latency of NPUs for LLM workloads.