Princeton U’s DataMUX Enables DNNs to Simultaneously and Accurately Process up to 40 Input Instances With Limited Computational Overhead | Synced
In the new paper DataMUX: Data Multiplexing for Neural Networks, a Princeton University research team proposes Data Multiplexing (DataMUX). The novel technique enables neural networks to process mu...
Source: Synced | AI Technology & Industry Review
In the new paper DataMUX: Data Multiplexing for Neural Networks, a Princeton University research team proposes Data Multiplexing (DataMUX). The novel technique enables neural networks to process multiple inputs simultaneously and generate accurate predictions, increasing model throughput with minimal additional memory requirements.
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