# Performance-Oriented Neural Architecture Search

arXiv: https://arxiv.org/abs/2001.02976 · HTML: https://mlsanity.com/paper/2001.02976/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [A Comprehensive Survey on Hardware-Aware Neural Architecture Search](https://mlsanity.com/paper/2101.09336/) (2021)
- [Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniques](https://mlsanity.com/paper/2112.15131/) (2021)
- [ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy](https://mlsanity.com/paper/2210.16083/) (2022)

## Abstract (arXiv)

Hardware-Software Co-Design is a highly successful strategy for improving performance of domain-specific computing systems. We argue for the application of the same methodology to deep learning; specifically, we propose to extend neural architecture search with information about the hardware to ensure that the model designs produced are highly efficient in addition to the typical criteria around accuracy. Using the task of keyword spotting in audio on edge computing devices, we demonstrate that our approach results in neural architecture that is not only highly accurate, but also efficiently mapped to the computing platform which will perform the inference. Using our modified neural architecture search, we demonstrate 0.88% increase in TOP-1 accuracy with 1.85× reduction in latency for keyword spotting in audio on an embedded SoC, and 1.59× on a high-end GPU.