# ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training

arXiv: https://arxiv.org/abs/2104.14129 · HTML: https://mlsanity.com/paper/2104.14129/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 54 references that are in the map.
- First posted on arXiv 29 Apr 2021.

## Builds on (most cited first, 30 of 38)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [Optimization Methods for Large-Scale Machine Learning](https://mlsanity.com/paper/1606.04838/) (2016)
- [Training Deep Nets with Sublinear Memory Cost](https://mlsanity.com/paper/1604.06174/) (2016)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Panoptic Feature Pyramid Networks](https://mlsanity.com/paper/1901.02446/) (2019)
- [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://mlsanity.com/paper/1511.00363/) (2015)
- [DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](https://mlsanity.com/paper/1606.06160/) (2016)
- [Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training](https://mlsanity.com/paper/1712.01887/) (2017)
- [PACT: Parameterized Clipping Activation for Quantized Neural Networks](https://mlsanity.com/paper/1805.06085/) (2018)
- [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://mlsanity.com/paper/2101.06840/) (2021)
- [LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks](https://mlsanity.com/paper/1807.10029/) (2018)
- [HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks](https://mlsanity.com/paper/1911.03852/) (2019)
- [Training Deep Neural Networks with 8-bit Floating Point Numbers](https://mlsanity.com/paper/1812.08011/) (2018)
- [Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization](https://mlsanity.com/paper/1910.02653/) (2019)
- [Mean Field Residual Networks: On the Edge of Chaos](https://mlsanity.com/paper/1712.08969/) (2017)

## Built on by (most cited first, 30 of 61)

- [Vchitect-2.0: Parallel Transformer for Scaling Up Video Diffusion Models](https://mlsanity.com/paper/2501.08453/) (2025)
- [A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking](https://mlsanity.com/paper/2210.07494/) (2022)
- [Memory Efficient Optimizers with 4-bit States](https://mlsanity.com/paper/2309.01507/) (2023)
- [A Survey on Efficient Training of Transformers](https://mlsanity.com/paper/2302.01107/) (2023)
- [Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities](https://mlsanity.com/paper/2210.06640/) (2022)
- [COAT: Compressing Optimizer states and Activation for Memory-Efficient FP8 Training](https://mlsanity.com/paper/2410.19313/) (2024)
- [GACT: Activation Compressed Training for Generic Network Architectures](https://mlsanity.com/paper/2206.11357/) (2022)
- [Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees](https://mlsanity.com/paper/2206.01299/) (2022)
- [POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging](https://mlsanity.com/paper/2207.07697/) (2022)
- [The Efficiency Spectrum of Large Language Models: An Algorithmic Survey](https://mlsanity.com/paper/2312.00678/) (2023)
- [TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge](https://mlsanity.com/paper/2307.09988/) (2023)
- [Mesa: A Memory-saving Training Framework for Transformers](https://mlsanity.com/paper/2111.11124/) (2021)
- [LLM-PQ: Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization](https://mlsanity.com/paper/2403.01136/) (2024)
- [Few-Bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction](https://mlsanity.com/paper/2202.00441/) (2022)
- [FedMef: Towards Memory-efficient Federated Dynamic Pruning](https://mlsanity.com/paper/2403.14737/) (2024)
- [Accurate INT8 Training Through Dynamic Block-Level Fallback](https://mlsanity.com/paper/2503.08040/) (2025)
- [LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms](https://mlsanity.com/paper/2311.11420/) (2023)
- [Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities](https://mlsanity.com/paper/2505.01043/) (2025)
- [QuantV2X: A Fully Quantized Multi-Agent System for Cooperative Perception](https://mlsanity.com/paper/2509.03704/) (2025)
- [Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training](https://mlsanity.com/paper/2306.01381/) (2023)
- [DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training](https://mlsanity.com/paper/2202.13808/) (2022)
- [Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model](https://mlsanity.com/paper/2305.15265/) (2023)
- [Do We Really Need a Large Number of Visual Prompts?](https://mlsanity.com/paper/2305.17223/) (2023)
- [End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml](https://mlsanity.com/paper/2501.14663/) (2025)
- [DipSVD: Dual-importance Protected SVD for Efficient LLM Compression](https://mlsanity.com/paper/2506.20353/) (2025)
- [Mist: Efficient Distributed Training of Large Language Models via Memory-Parallelism Co-Optimization](https://mlsanity.com/paper/2503.19050/) (2025)
- [OLLA: Optimizing the Lifetime and Location of Arrays to Reduce the Memory Usage of Neural Networks](https://mlsanity.com/paper/2210.12924/) (2022)
- [Memory-adaptive Depth-wise Heterogeneous Federated Learning](https://mlsanity.com/paper/2303.04887/) (2023)
- [SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization](https://mlsanity.com/paper/2412.04180/) (2024)
- [PaCA: Partial Connection Adaptation for Efficient Fine-Tuning](https://mlsanity.com/paper/2503.01905/) (2025)

## Abstract (arXiv)

The increasing size of neural network models has been critical for improvements in their accuracy, but device memory is not growing at the same rate. This creates fundamental challenges for training neural networks within limited memory environments. In this work, we propose ActNN, a memory-efficient training framework that stores randomly quantized activations for back propagation. We prove the convergence of ActNN for general network architectures, and we characterize the impact of quantization on the convergence via an exact expression for the gradient variance. Using our theory, we propose novel mixed-precision quantization strategies that exploit the activation's heterogeneity across feature dimensions, samples, and layers. These techniques can be readily applied to existing dynamic graph frameworks, such as PyTorch, simply by substituting the layers. We evaluate ActNN on mainstream computer vision models for classification, detection, and segmentation tasks. On all these tasks, ActNN compresses the activation to 2 bits on average, with negligible accuracy loss. ActNN reduces the memory footprint of the activation by 12x, and it enables training with a 6.6x to 14x larger batch size.