# Data Streaming and Traffic Gathering in Mesh-based NoC for Deep Neural Network Acceleration

arXiv: https://arxiv.org/abs/2108.02569 · HTML: https://mlsanity.com/paper/2108.02569/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 41 references that are in the map.
- First posted on arXiv 1 Aug 2021.

## Builds on (most cited first, 7 of 7)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [One weird trick for parallelizing convolutional neural networks](https://mlsanity.com/paper/1404.5997/) (2014)
- [DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving](https://mlsanity.com/paper/1505.00256/) (2015)
- [A 0.3-2.6 TOPS/W Precision-Scalable Processor for Real-Time Large-Scale ConvNets](https://mlsanity.com/paper/1606.05094/) (2016)
- [Hardware for Machine Learning: Challenges and Opportunities](https://mlsanity.com/paper/1612.07625/) (2016)

## Abstract (arXiv)

The increasing popularity of deep neural network (DNN) applications demands high computing power and efficient hardware accelerator architecture. DNN accelerators use a large number of processing elements (PEs) and on-chip memory for storing weights and other parameters. As the communication backbone of a DNN accelerator, networks-on-chip (NoC) play an important role in supporting various dataflow patterns and enabling processing with communication parallelism in a DNN accelerator. However, the widely used mesh-based NoC architectures inherently cannot support the efficient one-to-many and many-to-one traffic largely existing in DNN workloads. In this paper, we propose a modified mesh architecture with a one-way/two-way streaming bus to speedup one-to-many (multicast) traffic, and the use of gather packets to support many-to-one (gather) traffic. The analysis of the runtime latency of a convolutional layer shows that the two-way streaming architecture achieves better improvement than the one-way streaming architecture for an Output Stationary (OS) dataflow architecture. The simulation results demonstrate that the gather packets can help to reduce the runtime latency up to 1.8 times and network power consumption up to 1.7 times, compared with the repetitive unicast method on modified mesh architectures supporting two-way streaming.