# Bit Transition Reduction by Data Transmission Ordering in NoC-based DNN Accelerator

## TL;DR

This paper introduces a data transmission ordering method for NoC-based DNN accelerators that significantly reduces bit transitions, thereby lowering link power consumption across various data types and network configurations.

## Contribution

It proposes a novel '1'-bit count-based ordering method with mathematical proof, and two data ordering strategies, affiliated and separated, to effectively reduce bit transitions in DNN workloads.

## Key findings

- Up to 20.38% BT reduction for floating-point-32 data without NoC.
- Up to 55.71% BT reduction for fixed-point-8 data without NoC.
- Up to 40.85% BT reduction in NoC-based DNN accelerators.

## Abstract

As Deep Neural Networks (DNN) are becoming essential, Network-on-Chip (NoC)-based DNN accelerators gained increasing popularity. To save link power in NoC, many researchers focus on reducing the Bit Transition (BT). We propose '1'-bit count-based ordering method to reduce BT for DNN workloads. We provide a mathematical proof of the efficacy of proposed ordering. We evaluate our method through experiments without NoC and with NoC. Without NoC, our proposed ordering method achieves up to 20.38% BT reduction for floating-point-32 data and 55.71% for fixed-point-8 data, respectively. We propose two data ordering methods, affiliated-ordering and separated-ordering to process weight and input jointly or individually and apply them to run full DNNs in NoC-based DNN accelerator. We evaluate our approaches under various configurations, including different DNN models such as LeNet and DarkNet, various NoC sizes with different numbers of memory controllers, random weights and trained weights, and different data precision. Our approach efficiently reduces the link power by achieving up to 32.01% BT reduction for floating-point-32 data and 40.85% BT reduction for fixed-point-8 data.