# NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving

arXiv: https://arxiv.org/abs/1911.07446 · HTML: https://mlsanity.com/paper/1911.07446/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2019.

## Built on by (most cited first, 9 of 9)

- [HAO: Hardware-aware neural Architecture Optimization for Efficient Inference](https://mlsanity.com/paper/2104.12766/) (2021)
- [Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design](https://mlsanity.com/paper/2103.15750/) (2021)
- [SECDA: Efficient Hardware/Software Co-Design of FPGA-based DNN Accelerators for Edge Inference](https://mlsanity.com/paper/2110.00478/) (2021)
- [Software/Hardware Co-design for Multi-modal Multi-task Learning in Autonomous Systems](https://mlsanity.com/paper/2104.04000/) (2021)
- [MetaML-Pro: Cross-Stage Design Flow Automation for Efficient Deep Learning Acceleration](https://mlsanity.com/paper/2502.05850/) (2025)
- [How to Reach Real-Time AI on Consumer Devices? Solutions for Programmable and Custom Architectures](https://mlsanity.com/paper/2106.15021/) (2021)
- [HSCoNAS: Hardware-Software Co-Design of Efficient DNNs via Neural Architecture Search](https://mlsanity.com/paper/2103.08325/) (2021)
- [Generative Models and Connected and Automated Vehicles: A Survey in Exploring the Intersection of Transportation and AI](https://mlsanity.com/paper/2403.10559/) (2024)
- [Compilation and Optimizations for Efficient Machine Learning on Embedded Systems](https://mlsanity.com/paper/2206.03326/) (2022)

## Abstract (arXiv)

The rapidly growing demands for powerful AI algorithms in many application domains have motivated massive investment in both high-quality deep neural network (DNN) models and high-efficiency implementations. In this position paper, we argue that a simultaneous DNN/implementation co-design methodology, named Neural Architecture and Implementation Search (NAIS), deserves more research attention to boost the development productivity and efficiency of both DNN models and implementation optimization. We propose a stylized design methodology that can drastically cut down the search cost while preserving the quality of the end solution.As an illustration, we discuss this DNN/implementation methodology in the context of both FPGAs and GPUs. We take autonomous driving as a key use case as it is one of the most demanding areas for high quality AI algorithms and accelerators. We discuss how such a co-design methodology can impact the autonomous driving industry significantly. We identify several research opportunities in this exciting domain.