# Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation

arXiv: https://arxiv.org/abs/2001.08743 · HTML: https://mlsanity.com/paper/2001.08743/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2020.

## Built on by (most cited first, 29 of 29)

- [Tensor Program Optimization with Probabilistic Programs](https://mlsanity.com/paper/2205.13603/) (2022)
- [TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs](https://mlsanity.com/paper/2308.13490/) (2023)
- [Supersonic: Learning to Generate Source Code Optimizations in C/C++](https://mlsanity.com/paper/2309.14846/) (2023)
- [TLP: A Deep Learning-based Cost Model for Tensor Program Tuning](https://mlsanity.com/paper/2211.03578/) (2022)
- [Benchmarking of DL Libraries and Models on Mobile Devices](https://mlsanity.com/paper/2202.06512/) (2022)
- [MetaTune: Meta-Learning Based Cost Model for Fast and Efficient Auto-tuning Frameworks](https://mlsanity.com/paper/2102.04199/) (2021)
- [LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation](https://mlsanity.com/paper/2411.02322/) (2024)
- [Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation](https://mlsanity.com/paper/2201.05587/) (2022)
- [Empowering In-Browser Deep Learning Inference on Edge Devices with Just-in-Time Kernel Optimizations](https://mlsanity.com/paper/2309.08978/) (2023)
- [Dynamic Co-Optimization Compiler: Leveraging Multi-Agent Reinforcement Learning for Enhanced DNN Accelerator Performance](https://mlsanity.com/paper/2407.08192/) (2024)
- [Moses: Efficient Exploitation of Cross-device Transferable Features for Tensor Program Optimization](https://mlsanity.com/paper/2201.05752/) (2022)
- [HW-Aware Initialization of DNN Auto-Tuning to Improve Exploration Time and Robustness](https://mlsanity.com/paper/2205.15568/) (2022)
- [A Reinforcement Learning Environment for Automatic Code Optimization in the MLIR Compiler](https://mlsanity.com/paper/2409.11068/) (2024)
- [Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation](https://mlsanity.com/paper/2504.12984/) (2025)
- [REASONING COMPILER: LLM-Guided Optimizations for Efficient Model Serving](https://mlsanity.com/paper/2506.01374/) (2025)
- [HARL: Hierarchical Adaptive Reinforcement Learning Based Auto Scheduler for Neural Networks](https://mlsanity.com/paper/2211.11172/) (2022)
- [Joint Program and Layout Transformations to enable Convolutional Operators on Specialized Hardware based on Constraint Programming](https://mlsanity.com/paper/2104.04731/) (2021)
- [A Semi-Decoupled Approach to Fast and Optimal Hardware-Software Co-Design of Neural Accelerators](https://mlsanity.com/paper/2203.13921/) (2022)
- [Transfer Learning Across Heterogeneous Features For Efficient Tensor Program Generation](https://mlsanity.com/paper/2304.05430/) (2023)
- [Target-independent XLA optimization using Reinforcement Learning](https://mlsanity.com/paper/2308.14364/) (2023)
- [LoopTune: Optimizing Tensor Computations with Reinforcement Learning](https://mlsanity.com/paper/2309.01825/) (2023)
- [Shisha: Online scheduling of CNN pipelines on heterogeneous architectures](https://mlsanity.com/paper/2202.11575/) (2022)
- [ALT: Boosting Deep Learning Performance by Breaking the Wall between Graph and Operator Level Optimizations](https://mlsanity.com/paper/2210.12415/) (2022)
- [KAPLA: Pragmatic Representation and Fast Solving of Scalable NN Accelerator Dataflow](https://mlsanity.com/paper/2306.15676/) (2023)
- [GeoT: Tensor Centric Library for Graph Neural Network via Efficient Segment Reduction on GPU](https://mlsanity.com/paper/2404.03019/) (2024)
- [FTuner: A Fast Dynamic Shape Tensors Program Auto-Tuner for Deep Learning Compilers](https://mlsanity.com/paper/2407.21418/) (2024)
- [Data-efficient Performance Modeling via Pre-training](https://mlsanity.com/paper/2501.14438/) (2025)
- [DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architectures](https://mlsanity.com/paper/2511.15503/) (2025)
- [TCL: Enabling Fast and Efficient Cross-Hardware Tensor Program Optimization via Continual Learning](https://mlsanity.com/paper/2604.12891/) (2026)

## Abstract (arXiv)

Achieving faster execution with shorter compilation time can foster further diversity and innovation in neural networks. However, the current paradigm of executing neural networks either relies on hand-optimized libraries, traditional compilation heuristics, or very recently genetic algorithms and other stochastic methods. These methods suffer from frequent costly hardware measurements rendering them not only too time consuming but also suboptimal. As such, we devise a solution that can learn to quickly adapt to a previously unseen design space for code optimization, both accelerating the search and improving the output performance. This solution dubbed Chameleon leverages reinforcement learning whose solution takes fewer steps to converge, and develops an adaptive sampling algorithm that not only focuses on the costly samples (real hardware measurements) on representative points but also uses a domain-knowledge inspired logic to improve the samples itself. Experimentation with real hardware shows that Chameleon provides 4.45x speed up in optimization time over AutoTVM, while also improving inference time of the modern deep networks by 5.6%.