论文标题
Minun：微控制器的准确推断
MinUn: Accurate ML Inference on Microcontrollers
论文作者
论文摘要
在小型设备上的运行机器学习推断（称为Tinyml）是一个新兴的研究领域。此任务需要生成节俭使用内存的推理代码，这是标准ML框架不适合使用的任务。 Tinyml的部署框架必须为a）数字表示中的参数，以利用诸如PostIts，b）仔细地将高精度分配给几个张量的新兴表示，以便大多数张力张力可以保持低精度，同时保持模型的准确性，且c）避免记忆碎裂。我们描述了Minun，这是第一个整体上解决这些问题的tinyml框架，以生成有效的ARM微控制器代码（例如Arduino Uno，Due和STM32H747），以优于先前的Tinyml框架。
Running machine learning inference on tiny devices, known as TinyML, is an emerging research area. This task requires generating inference code that uses memory frugally, a task that standard ML frameworks are ill-suited for. A deployment framework for TinyML must be a) parametric in the number representation to take advantage of the emerging representations like posits, b) carefully assign high-precision to a few tensors so that most tensors can be kept in low-precision while still maintaining model accuracy, and c) avoid memory fragmentation. We describe MinUn, the first TinyML framework that holistically addresses these issues to generate efficient code for ARM microcontrollers (e.g., Arduino Uno, Due and STM32H747) that outperforms the prior TinyML frameworks.
