AxLaM:用于边缘计算的语言模型的节能加速器设计
Tom Glint1, Bhumika Mittal2, Santripta Sharma2
1Forschungszentrum Jülich, Jülich, Germany.
概括
本研究介绍了AxLaM,这是一个用于现代语言模型的节能硬件加速器. AxLaM显著降低了功耗,提高了性能,使得边缘设备上的先进人工智能成为可能.
科学领域:
- 计算机工程 计算机工程
- 人工智能的人工智能
- 硬件加速器 硬件加速器
背景情况:
- 现代语言模型,如来自变压器的双向编码器表示,在自然语言处理 (NLP) 中表现出色,但需要大量的电力.
- 它们的高计算需求阻碍了对资源有限的边缘设备的部署.
研究的目的:
- 为基于编码器的语言模型设计一个节能硬件加速器.
- 为了使先进的NLP功能能够集成到移动和边缘计算平台中.
主要方法:
- 开发了一个数据流意识的硬件加速器,AxLaM,灵感来自Simba架构.
- 嵌入了基于POSIT的近似固定点乘法器和高带宽内存 (HBM).
主要成果:
- 与Simba相比,AxLaM实现了9倍的能源减少,58%的面积减少和1.2倍的延迟改善.
- 证明了1.8 TOPS/W的能源效率,超过了FACT的65%.
结论:
- AxLaM提供了一个可行的解决方案,用于在边缘设备上部署计算密集型语言模型.
- 该设计显著提高了计算效率,并减少了边缘AI应用的功耗.
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