# PDPU: An Open-Source Posit Dot-Product Unit for Deep Learning Applications

arXiv: https://arxiv.org/abs/2302.01876 · HTML: https://mlsanity.com/paper/2302.01876/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 3 Feb 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Training Deep Neural Networks with 8-bit Floating Point Numbers](https://mlsanity.com/paper/1812.08011/) (2018)
- [Deep Positron: A Deep Neural Network Using the Posit Number System](https://mlsanity.com/paper/1812.01762/) (2018)
- [Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniques](https://mlsanity.com/paper/2112.15131/) (2021)
- [FPnew: An Open-Source Multi-Format Floating-Point Unit Architecture for Energy-Proportional Transprecision Computing](https://mlsanity.com/paper/2007.01530/) (2020)
- [DPU: DAG Processing Unit for Irregular Graphs with Precision-Scalable Posit Arithmetic in 28nm](https://mlsanity.com/paper/2112.05660/) (2021)
- [Training Deep Neural Networks Using Posit Number System](https://mlsanity.com/paper/1909.03831/) (2019)
- [Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit](https://mlsanity.com/paper/1805.08624/) (2018)
- [PositNN: Training Deep Neural Networks with Mixed Low-Precision Posit](https://mlsanity.com/paper/2105.00053/) (2021)

## Built on by (most cited first, 2 of 2)

- [SPADE: A SIMD Posit-enabled compute engine for Accelerating DNN Efficiency](https://mlsanity.com/paper/2601.17279/) (2026)
- [Enable Lightweight and Precision-Scalable Posit/IEEE-754 Arithmetic in RISC-V Cores for Transprecision Computing](https://mlsanity.com/paper/2505.19096/) (2025)

## Abstract (arXiv)

Posit has been a promising alternative to the IEEE-754 floating point format for deep learning applications due to its better trade-off between dynamic range and accuracy. However, hardware implementation of posit arithmetic requires further exploration, especially for the dot-product operations dominated in deep neural networks (DNNs). It has been implemented by either the combination of multipliers and an adder tree or cascaded fused multiply-add units, leading to poor computational efficiency and excessive hardware overhead. To address this issue, we propose an open-source posit dot-product unit, namely PDPU, that facilitates resource-efficient and high-throughput dot-product hardware implementation. PDPU not only features the fused and mixed-precision architecture that eliminates redundant latency and hardware resources, but also has a fine-grained 6-stage pipeline, improving computational efficiency. A configurable PDPU generator is further developed to meet the diverse needs of various DNNs for computational accuracy. Experimental results evaluated under the 28nm CMOS process show that PDPU reduces area, latency, and power by up to 43%, 64%, and 70%, respectively, compared to the existing implementations. Hence, PDPU has great potential as the computing core of posit-based accelerators for deep learning applications.