Many radar signal processing methodologies are being developed for critical
road safety perception tasks. Unfortunately, these signal processing algorithms
are often poorly suited to run on embedded hardware accelerators used in
automobiles. Conversely, end-to-end machine learning (ML) approaches better
exploit the performance gains brought by specialized accelerators. In this
paper, we propose a teacher-student knowledge distillation approach for
low-level radar perception tasks. We utilize a hybrid model for stationary
object detection as a teacher to train an end-to-end ML student model. The
student can efficiently harness embedded compute for real-time deployment. We
demonstrate that the proposed student model runs at speeds 100x faster than the
teacher model.

本文旨在提高雷达感知任务的嵌入式硬件加速器性能，通过利用一种新的教师 - 学生知识蒸馏方法进行低级别雷达感知任务的机器学习，使学生模型能够更好地利用特定的加速器带来的性能提升，实验结果表明所提出的学生模型比教师模型运行速度快 100 倍以上。

基于嵌入式加速器的雷达感知教师 - 学生知识蒸馏

Teacher-Student Knowledge Distillation for Radar Perception on Embedded Accelerators

Multilingual models have been widely used for cross-lingual transfer to
low-resource languages. However, the performance on these languages is hindered
by their underrepresentation in the pretraining data. To alleviate this
problem, we propose a novel multilingual training technique based on
teacher-student knowledge distillation. In this setting, we utilize monolingual
teacher models optimized for their language. We use those teachers along with
balanced (sub-sampled) data to distill the teachers' knowledge into a single
multilingual student. Our method outperforms standard training methods in
low-resource languages and retrains performance on high-resource languages
while using the same amount of data. If applied widely, our approach can
increase the representation of low-resource languages in NLP systems.

本文提出了一种基于教师 - 学生知识蒸馏的新型多语种训练技术，利用平衡（子采样）数据将单语教师模型的知识蒸馏到一个多语种学生中，可以提高自然语言处理系统中低资源语言的表现。