mlsanityData to 7 Oct 2026

Survey of Machine Learning Accelerators

arXiv 2009.00993 ↗First posted 1 Sep 2020cs.DC cs.LGcited by 29

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Abstract (arXiv)

New machine learning accelerators are being announced and released each month for a variety of applications from speech recognition, video object detection, assisted driving, and many data center applications. This paper updates the survey of of AI accelerators and processors from last year's IEEE-HPEC paper. This paper collects and summarizes the current accelerators that have been publicly announced with 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 discussed and analyzed. For instance, there are interesting trends in the plot regarding power consumption, numerical precision, and inference versus training. This year, there are many more announced accelerators that are implemented with many more architectures and technologies from vector engines, dataflow engines, neuromorphic designs, flash-based analog memory processing, and photonic-based processing.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Sep 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 5; 2023: 12; 2024: 1; 2025: 2; 2026: 2.

29 papers.

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  1. 2022Socratic Models: Composing Zero-Shot Multimodal Reasoning with Languagecited by 513
  2. 2021Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systemscited by 125
  3. 2023Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipelinecited by 50
  4. 2021An optical neural network using less than 1 photon per multiplicationcited by 30
  5. 2023Machine Learning Without a Processor: Emergent Learning in a Nonlinear Electronic Metamaterialcited by 19
  6. 2023TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollerscited by 18
  7. 2021AI Accelerator Survey and Trendscited by 17
  8. 2021Two Sparsities Are Better Than One: Unlocking the Performance Benefits of Sparse-Sparse Networkscited by 15
  9. 2023Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLOcited by 15
  10. 2023Optical Transformerscited by 10
  11. 2022HammingMesh: A Network Topology for Large-Scale Deep Learningcited by 8
  12. 2022Benchmarking Resource Usage for Efficient Distributed Deep Learningcited by 7
17 more
  1. 2023DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning Methodscited by 7
  2. 2024Asymmetrical estimator for training encapsulated deep photonic neural networkscited by 3
  3. 2021HiKonv: High Throughput Quantized Convolution With Novel Bit-wise Management and Computationcited by 2
  4. 2023DeepCAM: A Fully CAM-based Inference Accelerator with Variable Hash Lengths for Energy-efficient Deep Neural Networkscited by 2
  5. 2023Lincoln AI Computing Survey (LAICS) Updatecited by 2
  6. 2021Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctionscited by 1
  7. 2025Real-Time Semantic Segmentation of Aerial Images Using an Embedded U-Net: A Comparison of CPU, GPU, and FPGA Workflowscited by 1
  8. 2021A Construction Kit for Efficient Low Power Neural Network Accelerator Designscited by 0
  9. 2022A photonic chip-based machine learning approach for the prediction of molecular propertiescited by 0
  10. 2022HiKonv: Maximizing the Throughput of Quantized Convolution With Novel Bit-wise Management and Computationcited by 0
  11. 2023Exploiting Logic Locking for a Neural Trojan Attack on Machine Learning Acceleratorscited by 0
  12. 2023Convergence and scaling of Boolean-weight optimization for hardware reservoirscited by 0
  13. 2023mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR using Program Synthesiscited by 0
  14. 2023The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learningcited by 0
  15. 2025ML For Hardware Design Interpretability: Challenges and Opportunitiescited by 0
  16. 2026Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Modelscited by 0
  17. 2026A Holistic Assessment of the Carbon Footprint of Noor, a Very Large Arabic Language Modelcited by 0