mlsanityData to 7 Oct 2026

AI and ML Accelerator Survey and Trends

arXiv 2210.04055 ↗First posted 8 Oct 2022cs.ARcited by 23

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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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Oct 2022.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 9; 2024: 5; 2025: 5; 2026: 3.

23 papers.

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  1. 2023A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platformscited by 21
  2. 2022Intelligent Computing: The Latest Advances, Challenges and Futurecited by 6
  3. 2023PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainabilitycited by 6
  4. 2024FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compressioncited by 6
  5. 2025Advancing Earth Observation: A Survey on AI-Powered Image Processing in Satellitescited by 6
  6. 2023RedMule: A Mixed-Precision Matrix-Matrix Operation Engine for Flexible and Energy-Efficient On-Chip Linear Algebra and TinyML Training Accelerationcited by 4
  7. 2023Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computingcited by 3
  8. 2023Lincoln AI Computing Survey (LAICS) Updatecited by 2
  9. 2025Leveraging Neural Graph Compilers in Machine Learning Research for Edge-Cloud Systemscited by 2
  10. 2025In-memory Training on Analog Devices with Limited Conductance States via Multi-tile Residual Learningcited by 2
  11. 2023Subgraph Stationary Hardware-Software Inference Co-Designcited by 1
  12. 2023Stella Nera: A Differentiable Maddness-Based Hardware Accelerator for Efficient Approximate Matrix Multiplicationcited by 1
11 more
  1. 2024A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integrationcited by 1
  2. 2024When In-memory Computing Meets Spiking Neural Networks -- A Perspective on Device-Circuit-System-and-Algorithm Co-designcited by 1
  3. 2025eMamba: Efficient Acceleration Framework for Mamba Models in Edge Computingcited by 1
  4. 2026TrainDeeploy: Hardware-Accelerated Parameter-Efficient Fine-Tuning of Small Transformer Models at the Extreme Edgecited by 1
  5. 2026A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computingcited by 1
  6. 2023Hardware Accelerators in Autonomous Drivingcited by 0
  7. 2023Data Center Audio/Video Intelligence on Device (DAVID) -- An Edge-AI Platform for Smart-Toyscited by 0
  8. 2024A Precision-Optimized Fixed-Point Near-Memory Digital Processing Unit for Analog In-Memory Computingcited by 0
  9. 2024Optical Computing for Deep Neural Network Acceleration: Foundations, Recent Developments, and Emerging Directionscited by 0
  10. 2025LuxIA: A Lightweight Unitary matriX-based Framework Built on an Iterative Algorithm for Photonic Neural Network Trainingcited by 0
  11. 2026AI-RAN on NPUs: Baseband Processing Without Baseband Chipscited by 0