# MAPLE-X: Latency Prediction with Explicit Microprocessor Prior Knowledge

arXiv: https://arxiv.org/abs/2205.12660 · HTML: https://mlsanity.com/paper/2205.12660/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 6 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 5 of 5)

- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://mlsanity.com/paper/1812.00332/) (2018)
- [BRP-NAS: Prediction-based NAS using GCNs](https://mlsanity.com/paper/2007.08668/) (2020)
- [HELP: Hardware-Adaptive Efficient Latency Prediction for NAS via Meta-Learning](https://mlsanity.com/paper/2106.08630/) (2021)
- [MAPLE: Microprocessor A Priori for Latency Estimation](https://mlsanity.com/paper/2111.15106/) (2021)

## Abstract (arXiv)

Deep neural network (DNN) latency characterization is a time-consuming process and adds significant cost to Neural Architecture Search (NAS) processes when searching for efficient convolutional neural networks for embedded vision applications. DNN Latency is a hardware dependent metric and requires direct measurement or inference on target hardware. A recently introduced latency estimation technique known as MAPLE predicts DNN execution time on previously unseen hardware devices by using hardware performance counters. Leveraging these hardware counters in the form of an implicit prior, MAPLE achieves state-of-the-art performance in latency prediction. Here, we propose MAPLE-X which extends MAPLE by incorporating explicit prior knowledge of hardware devices and DNN architecture latency to better account for model stability and robustness. First, by identifying DNN architectures that exhibit a similar latency to each other, we can generate multiple virtual examples to significantly improve the accuracy over MAPLE. Secondly, the hardware specifications are used to determine the similarity between training and test hardware to emphasize training samples captured from comparable devices (domains) and encourages improved domain alignment. Experimental results using a convolution neural network NAS benchmark across different types of devices, including an Intel processor that is now used for embedded vision applications, demonstrate a 5% improvement over MAPLE and 9% over HELP. Furthermore, we include ablation studies to independently assess the benefits of virtual examples and hardware-based sample importance.