Training and inference on edge devices often requires an efficient setup due
to computational limitations. While pre-computing data representations and
caching them on a server can mitigate extensive edge device computation, this
leads to two challenges. First, the amount of storage required on the server
that scales linearly with the number of instances. Second, the bandwidth
required to send extensively large amounts of data to an edge device. To reduce
the memory footprint of pre-computed data representations, we propose a simple,
yet effective approach that uses randomly initialized hyperplane projections.
To further reduce their size by up to 98.96%, we quantize the resulting
floating-point representations into binary vectors. Despite the greatly reduced
size, we show that the embeddings remain effective for training models across
various English and German sentence classification tasks that retain 94%--99%
of their floating-point.

本研究提出了一种利用随机超平面投影和量化技术，减小预处理数据的存储和带宽需求，从而使得边缘设备可以高效地进行推理和训练，并且在多语言句子分类任务中保持了良好的性能。