Hardwired-Neurons Language Processing Units as General-Purpose Cognitive Substrates
Yang Liu, Yi Chen, Yongwei Zhao, Yifan Hao, Zifu Zheng, Weihao Kong, Zhangmai Li, Dongchen Jiang, Ruiyang Xia, Zhihong Ma, Zisheng Liu, Zhaoyong Wan, Yunqi Lu, Ximing Liu, Hongrui Guo, Zhihao Yang, Zhe Wang, Tianrui Ma, Mo Zou, Rui Zhang, Ling Li, Xing Hu, Zidong Du, Zhiwei Xu

TL;DR
This paper introduces a novel hardware approach called Hardwired-Neurons Language Processing Unit (HNLPU) that embeds LLM weights into metal wires, drastically improving efficiency and reducing costs and carbon footprint.
Contribution
The paper presents Metal-Embedding, a new 3D embedding technique that significantly reduces photomask costs, making hardwired LLM hardware feasible and efficient.
Findings
HNLPU achieves 249,960 tokens/sec processing speed.
HNLPU reduces cost-effectiveness by 41.7-80.4x compared to GPU clusters.
HNLPU cuts carbon footprint by 357x.
Abstract
The rapid advancement of Large Language Models (LLMs) has established language as a core general-purpose cognitive substrate, driving the demand for specialized Language Processing Units (LPUs) tailored for LLM inference. To overcome the growing energy consumption of LLM inference systems, this paper proposes a Hardwired-Neurons Language Processing Unit (HNLPU), which physically hardwires LLM weight parameters into the computational fabric, achieving several orders of magnitude computational efficiency improvement by extreme specialization. However, a significant challenge still lies in the scale of modern LLMs. A straightforward hardwiring of gpt-oss 120 B would require fabricating photomask sets valued at over 6 billion dollars, rendering this straightforward solution economically impractical. Addressing this challenge, we propose the novel Metal-Embedding methodology. Instead of…
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