# Apollo: Transferable Architecture Exploration

arXiv: https://arxiv.org/abs/2102.01723 · HTML: https://mlsanity.com/paper/2102.01723/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 2 Feb 2021.

## Builds on (most cited first, 13 of 13)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Constrained Bayesian Optimization with Noisy Experiments](https://mlsanity.com/paper/1706.07094/) (2017)
- [Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization](https://mlsanity.com/paper/1904.02642/) (2019)
- [Automatically designing CNN architectures using genetic algorithm for image classification](https://mlsanity.com/paper/1808.03818/) (2018)
- [A Survey on Practical Applications of Multi-Armed and Contextual Bandits](https://mlsanity.com/paper/1904.10040/) (2019)
- [Practical Design Space Exploration](https://mlsanity.com/paper/1810.05236/) (2018)
- [Accelerator-aware Neural Network Design using AutoML](https://mlsanity.com/paper/2003.02838/) (2020)
- [Population-Based Black-Box Optimization for Biological Sequence Design](https://mlsanity.com/paper/2006.03227/) (2020)
- [Transfer Learning based Dynamic Multiobjective Optimization Algorithms](https://mlsanity.com/paper/1612.06093/) (2016)
- [AutoAccel: Automated Accelerator Generation and Optimization with Composable, Parallel and Pipeline Architecture](https://mlsanity.com/paper/1809.07683/) (2018)
- [Bayesian Optimization with a Prior for the Optimum](https://mlsanity.com/paper/2006.14608/) (2020)

## Built on by (most cited first, 17 of 17)

- [A Survey of Machine Learning for Computer Architecture and Systems](https://mlsanity.com/paper/2102.07952/) (2021)
- [An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks](https://mlsanity.com/paper/2102.10423/) (2021)
- [Data-Driven Offline Optimization For Architecting Hardware Accelerators](https://mlsanity.com/paper/2110.11346/) (2021)
- [A Full-Stack Search Technique for Domain Optimized Deep Learning Accelerators](https://mlsanity.com/paper/2105.12842/) (2021)
- [Understanding LLM Embeddings for Regression](https://mlsanity.com/paper/2411.14708/) (2024)
- [Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization](https://mlsanity.com/paper/2207.13676/) (2022)
- [GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation](https://mlsanity.com/paper/2210.03894/) (2022)
- [AIRCHITECT: Learning Custom Architecture Design and Mapping Space](https://mlsanity.com/paper/2108.08295/) (2021)
- [DOSA: Differentiable Model-Based One-Loop Search for DNN Accelerators](https://mlsanity.com/paper/2509.10702/) (2025)
- [Tao: Re-Thinking DL-based Microarchitecture Simulation](https://mlsanity.com/paper/2404.10921/) (2024)
- [Concorde: Fast and Accurate CPU Performance Modeling with Compositional Analytical-ML Fusion](https://mlsanity.com/paper/2503.23076/) (2025)
- [A Learning-based Approach Towards Automated Tuning of SSD Configurations](https://mlsanity.com/paper/2110.08685/) (2021)
- [Athena: Synergizing Data Prefetching and Off-Chip Prediction via Online Reinforcement Learning](https://mlsanity.com/paper/2601.17615/) (2026)
- [AutoPilot: Automating SoC Design Space Exploration for SWaP Constrained Autonomous UAVs](https://mlsanity.com/paper/2102.02988/) (2021)
- [Chimera: A Hybrid Machine Learning Driven Multi-Objective Design Space Exploration Tool for FPGA High-Level Synthesis](https://mlsanity.com/paper/2207.07917/) (2022)
- [DCP: Learning Accelerator Dataflow for Neural Network via Propagation](https://mlsanity.com/paper/2410.06553/) (2024)
- [ChatNeuroSim: An LLM Agent Framework for Automated Compute-in-Memory Accelerator Deployment and Optimization](https://mlsanity.com/paper/2603.08745/) (2026)

## Abstract (arXiv)

The looming end of Moore's Law and ascending use of deep learning drives the design of custom accelerators that are optimized for specific neural architectures. Architecture exploration for such accelerators forms a challenging constrained optimization problem over a complex, high-dimensional, and structured input space with a costly to evaluate objective function. Existing approaches for accelerator design are sample-inefficient and do not transfer knowledge between related optimizations tasks with different design constraints, such as area and/or latency budget, or neural architecture configurations. In this work, we propose a transferable architecture exploration framework, dubbed Apollo, that leverages recent advances in black-box function optimization for sample-efficient accelerator design. We use this framework to optimize accelerator configurations of a diverse set of neural architectures with alternative design constraints. We show that our framework finds high reward design configurations (up to 24.6% speedup) more sample-efficiently than a baseline black-box optimization approach. We further show that by transferring knowledge between target architectures with different design constraints, Apollo is able to find optimal configurations faster and often with better objective value (up to 25% improvements). This encouraging outcome portrays a promising path forward to facilitate generating higher quality accelerators.