Research Paper#Large Language Models (LLMs) / Energy Efficiency / Hardware Acceleration🔬 ResearchAnalyzed: Jan 3, 2026 16:32
SRAM Size and Frequency Optimization for Energy-Efficient LLM Inference
Published:Dec 26, 2025 15:42
•1 min read
•ArXiv
Analysis
This paper is important because it provides concrete architectural insights for designing energy-efficient LLM accelerators. It highlights the trade-offs between SRAM size, operating frequency, and energy consumption in the context of LLM inference, particularly focusing on the prefill and decode phases. The findings are crucial for datacenter design, aiming to minimize energy overhead.
Key Takeaways
- •Larger SRAM buffers increase static energy due to leakage, which is not offset by latency benefits.
- •High operating frequencies can reduce total energy by reducing execution time and decreasing static energy consumption.
- •Memory bandwidth acts as a performance ceiling.
- •Optimal configuration: high frequency (1200-1400MHz) and small buffer (32-64KB) for best energy-delay product.
Reference
“Optimal hardware configuration: high operating frequencies (1200MHz-1400MHz) and a small local buffer size of 32KB to 64KB achieves the best energy-delay product.”