LLMSGHD: Co-Designing Large Language Model Software and Hardware for Efficient Inference

Abstract

Over the past two years, the rapid evolution of Large Language Models (LLMs) and the relatively slower progress in hardware development have led to the emergence of many operator-level optimization techniques and theories. These software-based methods accelerate model inference by improving memory management and computational efficiency, and they have demonstrated empirical effectiveness. However, in current computer architecture research, hardware designers often focus on microarchitectural optimizations or the performance of individual operations. They tend to adopt existing software optimizations passively, without actively leveraging them as design principles. Constrained by this limited perspective, researchers may overlook opportunities to systematically integrate insights from software into hardware design, potentially hindering more efficient and flexible architectural innovations. To combine operator-level optimization methods with hardware design, we introduce a hardware evaluation platform called LLMSGHD (Large Language Model Software-Guided Hardware Design), which focuses on operator-level optimized LLM inference workloads. LLMSGHD simulates hardware inference behavior while aiming for broad applicability and high efficiency. LLMSGHD integrates advanced software optimization techniques to offer more insightful analysis for hardware design, particularly regarding computational density variations in inference and their interaction with software-level optimizations. Based on LLMSGHD, we develope a heterogeneous LLM inference platform targeting high throughput and low cost.