# AI and ML Accelerator Survey and Trends

arXiv: https://arxiv.org/abs/2210.04055 · HTML: https://mlsanity.com/paper/2210.04055/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2022.

## Built on by (most cited first, 23 of 23)

- [A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms](https://mlsanity.com/paper/2306.15552/) (2023)
- [Advancing Earth Observation: A Survey on AI-Powered Image Processing in Satellites](https://mlsanity.com/paper/2501.12030/) (2025)
- [Intelligent Computing: The Latest Advances, Challenges and Future](https://mlsanity.com/paper/2211.11281/) (2022)
- [PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainability](https://mlsanity.com/paper/2310.03906/) (2023)
- [FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compression](https://mlsanity.com/paper/2403.16677/) (2024)
- [RedMule: A Mixed-Precision Matrix-Matrix Operation Engine for Flexible and Energy-Efficient On-Chip Linear Algebra and TinyML Training Acceleration](https://mlsanity.com/paper/2301.03904/) (2023)
- [Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computing](https://mlsanity.com/paper/2303.12910/) (2023)
- [In-memory Training on Analog Devices with Limited Conductance States via Multi-tile Residual Learning](https://mlsanity.com/paper/2510.02516/) (2025)
- [Lincoln AI Computing Survey (LAICS) Update](https://mlsanity.com/paper/2310.09145/) (2023)
- [Leveraging Neural Graph Compilers in Machine Learning Research for Edge-Cloud Systems](https://mlsanity.com/paper/2504.20198/) (2025)
- [A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing](https://mlsanity.com/paper/2607.16183/) (2026)
- [Subgraph Stationary Hardware-Software Inference Co-Design](https://mlsanity.com/paper/2306.17266/) (2023)
- [Stella Nera: A Differentiable Maddness-Based Hardware Accelerator for Efficient Approximate Matrix Multiplication](https://mlsanity.com/paper/2311.10207/) (2023)
- [A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integration](https://mlsanity.com/paper/2404.12498/) (2024)
- [When In-memory Computing Meets Spiking Neural Networks -- A Perspective on Device-Circuit-System-and-Algorithm Co-design](https://mlsanity.com/paper/2408.12767/) (2024)
- [eMamba: Efficient Acceleration Framework for Mamba Models in Edge Computing](https://mlsanity.com/paper/2508.10370/) (2025)
- [TrainDeeploy: Hardware-Accelerated Parameter-Efficient Fine-Tuning of Small Transformer Models at the Extreme Edge](https://mlsanity.com/paper/2603.09511/) (2026)
- [Hardware Accelerators in Autonomous Driving](https://mlsanity.com/paper/2308.06054/) (2023)
- [Data Center Audio/Video Intelligence on Device (DAVID) -- An Edge-AI Platform for Smart-Toys](https://mlsanity.com/paper/2311.11030/) (2023)
- [A Precision-Optimized Fixed-Point Near-Memory Digital Processing Unit for Analog In-Memory Computing](https://mlsanity.com/paper/2402.07549/) (2024)
- [Optical Computing for Deep Neural Network Acceleration: Foundations, Recent Developments, and Emerging Directions](https://mlsanity.com/paper/2407.21184/) (2024)
- [LuxIA: A Lightweight Unitary matriX-based Framework Built on an Iterative Algorithm for Photonic Neural Network Training](https://mlsanity.com/paper/2512.22264/) (2025)
- [AI-RAN on NPUs: Baseband Processing Without Baseband Chips](https://mlsanity.com/paper/2607.04224/) (2026)

## Abstract (arXiv)

This paper updates the survey of AI accelerators and processors from past three years. This paper collects and summarizes the current commercial accelerators that have been publicly announced with peak performance and power consumption numbers. The performance and power values are plotted on a scatter graph, and a number of dimensions and observations from the trends on this plot are again discussed and analyzed. Two new trends plots based on accelerator release dates are included in this year's paper, along with the additional trends of some neuromorphic, photonic, and memristor-based inference accelerators.