# Neural Architecture Search for Improving Latency-Accuracy Trade-off in Split Computing

arXiv: https://arxiv.org/abs/2208.13968 · HTML: https://mlsanity.com/paper/2208.13968/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 13 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Builds on (most cited first, 9 of 9)

- [FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search](https://mlsanity.com/paper/1812.03443/) (2018)
- [NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search](https://mlsanity.com/paper/2001.00326/) (2020)
- [Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges](https://mlsanity.com/paper/2103.04505/) (2021)
- [HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark](https://mlsanity.com/paper/2103.10584/) (2021)
- [BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services](https://mlsanity.com/paper/1902.01000/) (2019)
- [Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search](https://mlsanity.com/paper/1905.08537/) (2019)
- [Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge](https://mlsanity.com/paper/1812.06426/) (2018)
- [Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networks](https://mlsanity.com/paper/2112.09407/) (2021)
- [Efficient Search of Multiple Neural Architectures with Different Complexities via Importance Sampling](https://mlsanity.com/paper/2207.10334/) (2022)

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

- [Lambda-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative AI](https://mlsanity.com/paper/2310.14651/) (2023)
- [SALT: A Lightweight Model Adaptation Method for Closed Split Computing Environments](https://mlsanity.com/paper/2506.07355/) (2025)
- [Lightweight User-Personalization Method for Closed Split Computing](https://mlsanity.com/paper/2603.14958/) (2026)

## Abstract (arXiv)

This paper proposes a neural architecture search (NAS) method for split computing. Split computing is an emerging machine-learning inference technique that addresses the privacy and latency challenges of deploying deep learning in IoT systems. In split computing, neural network models are separated and cooperatively processed using edge servers and IoT devices via networks. Thus, the architecture of the neural network model significantly impacts the communication payload size, model accuracy, and computational load. In this paper, we address the challenge of optimizing neural network architecture for split computing. To this end, we proposed NASC, which jointly explores optimal model architecture and a split point to achieve higher accuracy while meeting latency requirements (i.e., smaller total latency of computation and communication than a certain threshold). NASC employs a one-shot NAS that does not require repeating model training for a computationally efficient architecture search. Our performance evaluation using hardware (HW)-NAS-Bench of benchmark data demonstrates that the proposed NASC can improve the ``communication latency and model accuracy" trade-off, i.e., reduce the latency by approximately 40-60% from the baseline, with slight accuracy degradation.