# EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators

arXiv: https://arxiv.org/abs/1908.11645 · HTML: https://mlsanity.com/paper/1908.11645/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [Reimagining Memory Access for LLM Inference: Compression-Aware Memory Controller Design](https://mlsanity.com/paper/2503.18869/) (2025)
- [Vau da muntanialas: Energy-efficient multi-die scalable acceleration of RNN inference](https://mlsanity.com/paper/2202.07462/) (2022)
- [Improved Projection Learning for Lower Dimensional Feature Maps](https://mlsanity.com/paper/2210.15170/) (2022)
- [ASC: Adaptive Scale Feature Map Compression for Deep Neural Network](https://mlsanity.com/paper/2312.08176/) (2023)

## Abstract (arXiv)

In the wake of the success of convolutional neural networks in image classification, object recognition, speech recognition, etc., the demand for deploying these compute-intensive ML models on embedded and mobile systems with tight power and energy constraints at low cost, as well as for boosting throughput in data centers, is growing rapidly. This has sparked a surge of research into specialized hardware accelerators. Their performance is typically limited by I/O bandwidth, power consumption is dominated by I/O transfers to off-chip memory, and on-chip memories occupy a large part of the silicon area. We introduce and evaluate a novel, hardware-friendly, and lossless compression scheme for the feature maps present within convolutional neural networks. We present hardware architectures and synthesis results for the compressor and decompressor in 65nm. With a throughput of one 8-bit word/cycle at 600MHz, they fit into 2.8kGE and 3.0kGE of silicon area, respectively - together the size of less than seven 8-bit multiply-add units at the same throughput. We show that an average compression ratio of 5.1x for AlexNet, 4x for VGG-16, 2.4x for ResNet-34 and 2.2x for MobileNetV2 can be achieved - a gain of 45-70% over existing methods. Our approach also works effectively for various number formats, has a low frame-to-frame variance on the compression ratio, and achieves compression factors for gradient map compression during training that are even better than for inference.