# MAPLE: Microprocessor A Priori for Latency Estimation

arXiv: https://arxiv.org/abs/2111.15106 · HTML: https://mlsanity.com/paper/2111.15106/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 44 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Pruning Filters for Efficient ConvNets](https://mlsanity.com/paper/1608.08710/) (2016)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [Once-for-All: Train One Network and Specialize it for Efficient Deployment](https://mlsanity.com/paper/1908.09791/) (2019)
- [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://mlsanity.com/paper/1812.00332/) (2018)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Progressive Neural Architecture Search](https://mlsanity.com/paper/1712.00559/) (2017)
- [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)
- [AMC: AutoML for Model Compression and Acceleration on Mobile Devices](https://mlsanity.com/paper/1802.03494/) (2018)
- [Designing Neural Network Architectures using Reinforcement Learning](https://mlsanity.com/paper/1611.02167/) (2016)
- [PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search](https://mlsanity.com/paper/1907.05737/) (2019)
- [FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions](https://mlsanity.com/paper/2004.05565/) (2020)
- [HAT: Hardware-Aware Transformers for Efficient Natural Language Processing](https://mlsanity.com/paper/2005.14187/) (2020)
- [BRP-NAS: Prediction-based NAS using GCNs](https://mlsanity.com/paper/2007.08668/) (2020)
- [ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation](https://mlsanity.com/paper/1812.08934/) (2018)
- [DrNAS: Dirichlet Neural Architecture Search](https://mlsanity.com/paper/2006.10355/) (2020)
- [A Study on Encodings for Neural Architecture Search](https://mlsanity.com/paper/2007.04965/) (2020)
- [Fast Hardware-Aware Neural Architecture Search](https://mlsanity.com/paper/1910.11609/) (2019)
- [Task Transfer by Preference-Based Cost Learning](https://mlsanity.com/paper/1805.04686/) (2018)
- [AOWS: Adaptive and optimal network width search with latency constraints](https://mlsanity.com/paper/2005.10481/) (2020)
- [HELP: Hardware-Adaptive Efficient Latency Prediction for NAS via Meta-Learning](https://mlsanity.com/paper/2106.08630/) (2021)

## Built on by (most cited first, 5 of 5)

- [MAPLE-Edge: A Runtime Latency Predictor for Edge Devices](https://mlsanity.com/paper/2204.12950/) (2022)
- [Inference Latency Prediction at the Edge](https://mlsanity.com/paper/2210.02620/) (2022)
- [MAPLE-X: Latency Prediction with Explicit Microprocessor Prior Knowledge](https://mlsanity.com/paper/2205.12660/) (2022)
- [AccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Training](https://mlsanity.com/paper/2311.05827/) (2023)
- [CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening](https://mlsanity.com/paper/2608.10506/) (2026)

## Abstract (arXiv)

Modern deep neural networks must demonstrate state-of-the-art accuracy while exhibiting low latency and energy consumption. As such, neural architecture search (NAS) algorithms take these two constraints into account when generating a new architecture. However, efficiency metrics such as latency are typically hardware dependent requiring the NAS algorithm to either measure or predict the architecture latency. Measuring the latency of every evaluated architecture adds a significant amount of time to the NAS process. Here we propose Microprocessor A Priori for Latency Estimation MAPLE that does not rely on transfer learning or domain adaptation but instead generalizes to new hardware by incorporating a prior hardware characteristics during training. MAPLE takes advantage of a novel quantitative strategy to characterize the underlying microprocessor by measuring relevant hardware performance metrics, yielding a fine-grained and expressive hardware descriptor. Moreover, the proposed MAPLE benefits from the tightly coupled I/O between the CPU and GPU and their dependency to predict DNN latency on GPUs while measuring microprocessor performance hardware counters from the CPU feeding the GPU hardware. Through this quantitative strategy as the hardware descriptor, MAPLE can generalize to new hardware via a few shot adaptation strategy where with as few as 3 samples it exhibits a 6% improvement over state-of-the-art methods requiring as much as 10 samples. Experimental results showed that, increasing the few shot adaptation samples to 10 improves the accuracy significantly over the state-of-the-art methods by 12%. Furthermore, it was demonstrated that MAPLE exhibiting 8-10% better accuracy, on average, compared to relevant baselines at any number of adaptation samples.