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

## TL;DR

Deep Positron introduces a low-precision neural network architecture using the posit number system, achieving better accuracy and efficiency at 8 bits or less compared to traditional formats.

## Contribution

The paper presents a novel DNN architecture with posit numerical format at ≤8 bits, including a precision-adaptable FPGA soft core for exact multiply-and-accumulate operations.

## Key findings

- 8-bit posit outperforms fixed and floating-point in accuracy
- 8-bit posit achieves comparable accuracy to 32-bit floating-point
- Posit offers better accuracy and latency trade-offs at low bit-widths

## Abstract

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 $\leq$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.