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Training and inference on edge devices often requires an efficient setup dueto computational limitations. While pre-computing data representations andcaching them on a server can mitigate extensive edge device computation, thisleads to two challenges. First, the amount of storage required on the serverthat scales linearly with the number of instances. Second, the bandwidthrequired to send extensively large amounts of data to an edge device. To reducethe 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 resultingfloating-point representations into binary vectors. Despite the greatly reducedsize, we show that the embeddings remain effective for training models acrossvarious English and German sentence classification tasks that retain 94%--99%of their floating-point.

Rediscovering Hashed Random Projections for Efficient Quantization of Contextualized Sentence Embeddings