Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.17862 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917420902383616 |
|---|---|
| author | Xie, Yan Mao, Changkui Wu, Changsong Lu, Chao Suo, Chao Qian, Cheng Yang, Chun Zhu, Danyang Xiong, Hengchang Lu, Hongzhan Liu, Hongzhen Liu, Jiafu Chen, Jie Dai, Jie Tang, Junfeng Liu, Kai Li, Kun Ge, Lipeng Sun, Meng Luo, Min Chen, Peng Wang, Peng Yang, Shaodong Tang, Shibin Chen, Shibo Zhang, Weikang Ling, Xiao Du, Xiaobo Wu, Xin Liu, Yang Jiang, Yi Jin, Yihua Huang, Yin Zhang, Yuli Yuan, Zhen Man, Zhiyuan Yao, Zhongxiao |
| author_facet | Xie, Yan Mao, Changkui Wu, Changsong Lu, Chao Suo, Chao Qian, Cheng Yang, Chun Zhu, Danyang Xiong, Hengchang Lu, Hongzhan Liu, Hongzhen Liu, Jiafu Chen, Jie Dai, Jie Tang, Junfeng Liu, Kai Li, Kun Ge, Lipeng Sun, Meng Luo, Min Chen, Peng Wang, Peng Yang, Shaodong Tang, Shibin Chen, Shibo Zhang, Weikang Ling, Xiao Du, Xiaobo Wu, Xin Liu, Yang Jiang, Yi Jin, Yihua Huang, Yin Zhang, Yuli Yuan, Zhen Man, Zhiyuan Yao, Zhongxiao |
| contents | As deep learning-based AI technologies gain momentum, the demand for general-purpose AI computing architectures continues to grow. While GPGPU-based architectures offer versatility for diverse AI workloads, they often fall short in efficiency and cost-effectiveness. Various Domain-Specific Architectures (DSAs) excel at particular AI tasks but struggle to extend across broader applications or adapt to the rapidly evolving AI landscape. M100 is Li Auto's response: a performant, cost-effective architecture for AI inference in Autonomous Driving (AD), Large Language Models (LLMs), and intelligent human interactions, domains crucial to today's most competitive automobile platforms. M100 employs a dataflow parallel architecture, where compiler-architecture co-design orchestrates not only computation but, more critically, data movement across time and space. Leveraging dataflow computing efficiency, our hardware-software co-design improves system performance while reducing hardware complexity and cost. M100 largely eliminates caching: tensor computations are driven by compiler- and runtime-managed data streams flowing between computing elements and on/off-chip memories, yielding greater efficiency and scalability than cache-based systems. Another key principle was selecting the right operational granularity for scheduling, issuing, and execution across compiler, firmware, and hardware. Recognizing commonalities in AI workloads, we chose the tensor as the fundamental data element. M100 demonstrates general AI computing capability across diverse inference applications, including UniAD (for AD) and LLaMA (for LLMs). Benchmarks show M100 outperforms GPGPU architectures in AD applications with higher utilization, representing a promising direction for future general AI computing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17862 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | M100: An Orchestrated Dataflow Architecture Powering General AI Computing Xie, Yan Mao, Changkui Wu, Changsong Lu, Chao Suo, Chao Qian, Cheng Yang, Chun Zhu, Danyang Xiong, Hengchang Lu, Hongzhan Liu, Hongzhen Liu, Jiafu Chen, Jie Dai, Jie Tang, Junfeng Liu, Kai Li, Kun Ge, Lipeng Sun, Meng Luo, Min Chen, Peng Wang, Peng Yang, Shaodong Tang, Shibin Chen, Shibo Zhang, Weikang Ling, Xiao Du, Xiaobo Wu, Xin Liu, Yang Jiang, Yi Jin, Yihua Huang, Yin Zhang, Yuli Yuan, Zhen Man, Zhiyuan Yao, Zhongxiao Machine Learning Hardware Architecture As deep learning-based AI technologies gain momentum, the demand for general-purpose AI computing architectures continues to grow. While GPGPU-based architectures offer versatility for diverse AI workloads, they often fall short in efficiency and cost-effectiveness. Various Domain-Specific Architectures (DSAs) excel at particular AI tasks but struggle to extend across broader applications or adapt to the rapidly evolving AI landscape. M100 is Li Auto's response: a performant, cost-effective architecture for AI inference in Autonomous Driving (AD), Large Language Models (LLMs), and intelligent human interactions, domains crucial to today's most competitive automobile platforms. M100 employs a dataflow parallel architecture, where compiler-architecture co-design orchestrates not only computation but, more critically, data movement across time and space. Leveraging dataflow computing efficiency, our hardware-software co-design improves system performance while reducing hardware complexity and cost. M100 largely eliminates caching: tensor computations are driven by compiler- and runtime-managed data streams flowing between computing elements and on/off-chip memories, yielding greater efficiency and scalability than cache-based systems. Another key principle was selecting the right operational granularity for scheduling, issuing, and execution across compiler, firmware, and hardware. Recognizing commonalities in AI workloads, we chose the tensor as the fundamental data element. M100 demonstrates general AI computing capability across diverse inference applications, including UniAD (for AD) and LLaMA (for LLMs). Benchmarks show M100 outperforms GPGPU architectures in AD applications with higher utilization, representing a promising direction for future general AI computing. |
| title | M100: An Orchestrated Dataflow Architecture Powering General AI Computing |
| topic | Machine Learning Hardware Architecture |
| url | https://arxiv.org/abs/2604.17862 |