# AI Accelerator Survey and Trends

arXiv: https://arxiv.org/abs/2109.08957 · HTML: https://mlsanity.com/paper/2109.08957/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 134 references that are in the map.
- First posted on arXiv 18 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [EIE: Efficient Inference Engine on Compressed Deep Neural Network](https://mlsanity.com/paper/1602.01528/) (2016)
- [MLPerf Inference Benchmark](https://mlsanity.com/paper/1911.02549/) (2019)
- [An Analysis of Deep Neural Network Models for Practical Applications](https://mlsanity.com/paper/1605.07678/) (2016)
- [Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing](https://mlsanity.com/paper/1603.08270/) (2016)
- [Survey of Machine Learning Accelerators](https://mlsanity.com/paper/2009.00993/) (2020)
- [DLA: Compiler and FPGA Overlay for Neural Network Inference Acceleration](https://mlsanity.com/paper/1807.06434/) (2018)
- [Deep Neural Network Approximation for Custom Hardware: Where We've Been, Where We're Going](https://mlsanity.com/paper/1901.06955/) (2019)
- [Kickstarting High-performance Energy-efficient Manycore Architectures with Epiphany](https://mlsanity.com/paper/1412.5538/) (2014)
- [Epiphany-V: A 1024 processor 64-bit RISC System-On-Chip](https://mlsanity.com/paper/1610.01832/) (2016)

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

- [Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators](https://mlsanity.com/paper/2302.08469/) (2023)
- [Intelligent Computing: The Latest Advances, Challenges and Future](https://mlsanity.com/paper/2211.11281/) (2022)
- [The MIT Supercloud Workload Classification Challenge](https://mlsanity.com/paper/2204.05839/) (2022)
- [Precision-aware Latency and Energy Balancing on Multi-Accelerator Platforms for DNN Inference](https://mlsanity.com/paper/2306.05060/) (2023)
- [PINCH: An Adversarial Extraction Attack Framework for Deep Learning Models](https://mlsanity.com/paper/2209.06300/) (2022)
- [Statistical Modeling of Soft Error Influence on Neural Networks](https://mlsanity.com/paper/2210.05876/) (2022)
- [One-Shot Online Testing of Deep Neural Networks Based on Distribution Shift Detection](https://mlsanity.com/paper/2305.09348/) (2023)
- [Lincoln AI Computing Survey (LAICS) Update](https://mlsanity.com/paper/2310.09145/) (2023)
- [AI-assisted Automated Workflow for Real-time X-ray Ptychography Data Analysis via Federated Resources](https://mlsanity.com/paper/2304.04297/) (2023)
- [Energy Estimates Across Layers of Computing: From Devices to Large-Scale Applications in Machine Learning for Natural Language Processing, Scientific Computing, and Cryptocurrency Mining](https://mlsanity.com/paper/2310.07516/) (2023)
- [GreenLightningAI: An Efficient AI System with Decoupled Structural and Quantitative Knowledge](https://mlsanity.com/paper/2312.09971/) (2023)
- [Machines & Influence: An Information Systems Lens](https://mlsanity.com/paper/2111.13365/) (2021)
- [Perspectives on AI Architectures and Co-design for Earth System Predictability](https://mlsanity.com/paper/2304.03748/) (2023)
- [Optimizing DNN Inference on Multi-Accelerator SoCs at Training-time](https://mlsanity.com/paper/2409.18566/) (2024)
- [Rethinking Inference Placement for Deep Learning across Edge and Cloud Platforms: A Multi-Objective Optimization Perspective and Future Directions](https://mlsanity.com/paper/2510.22909/) (2025)
- [DCO: Dynamic Cache Orchestration for LLM Accelerators through Predictive Management](https://mlsanity.com/paper/2512.07312/) (2025)
- [A Holistic Assessment of the Carbon Footprint of Noor, a Very Large Arabic Language Model](https://mlsanity.com/paper/2610.00223/) (2026)

## Abstract (arXiv)

Over the past several years, new machine learning accelerators were being announced and released every 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 AI accelerators and processors from past two 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. This year, we also compile a list of benchmarking performance results and compute the computational efficiency with respect to peak performance.