# Deep Positron: A Deep Neural Network Using the Posit Number System

arXiv: https://arxiv.org/abs/1812.01762 · HTML: https://mlsanity.com/paper/1812.01762/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2018.

## Built on by (most cited first, 13 of 13)

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- [PLAM: a Posit Logarithm-Approximate Multiplier](https://mlsanity.com/paper/2102.09262/) (2021)
- [PositNN: Training Deep Neural Networks with Mixed Low-Precision Posit](https://mlsanity.com/paper/2105.00053/) (2021)
- [PDPU: An Open-Source Posit Dot-Product Unit for Deep Learning Applications](https://mlsanity.com/paper/2302.01876/) (2023)
- [Efficient and Mathematically Robust Operations for Certified Neural Networks Inference](https://mlsanity.com/paper/2401.08225/) (2024)
- [EntroLLM: Entropy Encoded Weight Compression for Efficient Large Language Model Inference on Edge Devices](https://mlsanity.com/paper/2505.02380/) (2025)
- [Compressed Real Numbers for AI: a case-study using a RISC-V CPU](https://mlsanity.com/paper/2309.07158/) (2023)

## Abstract (arXiv)

The recent surge of interest in Deep Neural Networks (DNNs) has led to increasingly complex networks that tax computational and memory resources. Many DNNs presently use 16-bit or 32-bit floating point operations. Significant performance and power gains can be obtained when DNN accelerators support low-precision numerical formats. Despite considerable research, there is still a knowledge gap on how low-precision operations can be realized for both DNN training and inference. In this work, we propose a DNN architecture, Deep Positron, with posit numerical format operating successfully at ≤8 bits for inference. We propose a precision-adaptable FPGA soft core for exact multiply-and-accumulate for uniform comparison across three numerical formats, fixed, floating-point and posit. Preliminary results demonstrate that 8-bit posit has better accuracy than 8-bit fixed or floating-point for three different low-dimensional datasets. Moreover, the accuracy is comparable to 32-bit floating-point on a Xilinx Virtex-7 FPGA device. The trade-offs between DNN performance and hardware resources, i.e. latency, power, and resource utilization, show that posit outperforms in accuracy and latency at 8-bit and below.