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Satisfying the high computation demand of modern deep learning architecturesis challenging for achieving low inference latency. The current approaches indecreasing latency only increase parallelism within a layer. This is becausearchitectures typically capture a single-chain dependency pattern that preventsefficient distribution with a higher concurrency (i.e., simultaneous executionof one inference among devices). Such single-chain dependencies are sowidespread that even implicitly biases recent neural architecture search (NAS)studies. In this visionary paper, we draw attention to an entirely new space ofNAS that relaxes the single-chain dependency to provide higher concurrency anddistribution opportunities. To quantitatively compare these architectures, wepropose a score that encapsulates crucial metrics such as communication,concurrency, and load balancing. Additionally, we propose a new generator andtransformation block that consistently deliver superior architectures comparedto current state-of-the-art methods. Finally, our preliminary results show thatthese new architectures reduce the inference latency and deserve moreattention.

Reducing Inference Latency with Concurrent Architectures for Image Recognition