# Performance-Efficiency Trade-off of Low-Precision Numerical Formats in Deep Neural Networks

## TL;DR

This paper analyzes the trade-offs between different low-precision numerical formats for deep neural network inference, demonstrating that posit formats outperform others at 8-bit or less precision with efficient resource use.

## Contribution

The study provides a unified analysis of fixed-point, floating point, and posit formats at ≤8-bit precision in DNN accelerators, highlighting the advantages of posit formats.

## Key findings

- Posit formats outperform other low-precision formats at ≤8-bit.
- Posit-based DNN inference achieves competitive resource efficiency.
- Trade-offs between efficiency and performance are quantified across five tasks.

## Abstract

Deep neural networks (DNNs) have been demonstrated as effective prognostic models across various domains, e.g. natural language processing, computer vision, and genomics. However, modern-day DNNs demand high compute and memory storage for executing any reasonably complex task. To optimize the inference time and alleviate the power consumption of these networks, DNN accelerators with low-precision representations of data and DNN parameters are being actively studied. An interesting research question is in how low-precision networks can be ported to edge-devices with similar performance as high-precision networks. In this work, we employ the fixed-point, floating point, and posit numerical formats at $\leq$8-bit precision within a DNN accelerator, Deep Positron, with exact multiply-and-accumulate (EMAC) units for inference. A unified analysis quantifies the trade-offs between overall network efficiency and performance across five classification tasks. Our results indicate that posits are a natural fit for DNN inference, outperforming at $\leq$8-bit precision, and can be realized with competitive resource requirements relative to those of floating point.