# Survey of Machine Learning Accelerators

arXiv: https://arxiv.org/abs/2009.00993 · HTML: https://mlsanity.com/paper/2009.00993/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2020.

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

- [Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language](https://mlsanity.com/paper/2204.00598/) (2022)
- [Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems](https://mlsanity.com/paper/2104.13386/) (2021)
- [Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline](https://mlsanity.com/paper/2304.06793/) (2023)
- [An optical neural network using less than 1 photon per multiplication](https://mlsanity.com/paper/2104.13467/) (2021)
- [Machine Learning Without a Processor: Emergent Learning in a Nonlinear Electronic Metamaterial](https://mlsanity.com/paper/2311.00537/) (2023)
- [TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers](https://mlsanity.com/paper/2306.00001/) (2023)
- [AI Accelerator Survey and Trends](https://mlsanity.com/paper/2109.08957/) (2021)
- [Two Sparsities Are Better Than One: Unlocking the Performance Benefits of Sparse-Sparse Networks](https://mlsanity.com/paper/2112.13896/) (2021)
- [Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLO](https://mlsanity.com/paper/2311.01057/) (2023)
- [Optical Transformers](https://mlsanity.com/paper/2302.10360/) (2023)
- [HammingMesh: A Network Topology for Large-Scale Deep Learning](https://mlsanity.com/paper/2209.01346/) (2022)
- [Benchmarking Resource Usage for Efficient Distributed Deep Learning](https://mlsanity.com/paper/2201.12423/) (2022)
- [DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning Methods](https://mlsanity.com/paper/2303.04025/) (2023)
- [HiKonv: High Throughput Quantized Convolution With Novel Bit-wise Management and Computation](https://mlsanity.com/paper/2112.13972/) (2021)
- [Asymmetrical estimator for training encapsulated deep photonic neural networks](https://mlsanity.com/paper/2405.18458/) (2024)
- [DeepCAM: A Fully CAM-based Inference Accelerator with Variable Hash Lengths for Energy-efficient Deep Neural Networks](https://mlsanity.com/paper/2302.04712/) (2023)
- [Lincoln AI Computing Survey (LAICS) Update](https://mlsanity.com/paper/2310.09145/) (2023)
- [Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions](https://mlsanity.com/paper/2112.09159/) (2021)
- [Real-Time Semantic Segmentation of Aerial Images Using an Embedded U-Net: A Comparison of CPU, GPU, and FPGA Workflows](https://mlsanity.com/paper/2503.08700/) (2025)
- [A Construction Kit for Efficient Low Power Neural Network Accelerator Designs](https://mlsanity.com/paper/2106.12810/) (2021)
- [A photonic chip-based machine learning approach for the prediction of molecular properties](https://mlsanity.com/paper/2203.02285/) (2022)
- [HiKonv: Maximizing the Throughput of Quantized Convolution With Novel Bit-wise Management and Computation](https://mlsanity.com/paper/2208.00763/) (2022)
- [Exploiting Logic Locking for a Neural Trojan Attack on Machine Learning Accelerators](https://mlsanity.com/paper/2304.06017/) (2023)
- [Convergence and scaling of Boolean-weight optimization for hardware reservoirs](https://mlsanity.com/paper/2305.07908/) (2023)
- [mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR using Program Synthesis](https://mlsanity.com/paper/2310.04196/) (2023)
- [The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning](https://mlsanity.com/paper/2312.03120/) (2023)
- [ML For Hardware Design Interpretability: Challenges and Opportunities](https://mlsanity.com/paper/2504.08852/) (2025)
- [Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models](https://mlsanity.com/paper/2603.08173/) (2026)
- [A Holistic Assessment of the Carbon Footprint of Noor, a Very Large Arabic Language Model](https://mlsanity.com/paper/2610.00223/) (2026)

## 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.