# Improvements in Interlayer Pipelining of CNN Accelerators Using Genetic Algorithms

arXiv: https://arxiv.org/abs/2311.12235 · HTML: https://mlsanity.com/paper/2311.12235/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 20 Nov 2023.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [TinyViT: Fast Pretraining Distillation for Small Vision Transformers](https://mlsanity.com/paper/2207.10666/) (2022)
- [An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks](https://mlsanity.com/paper/2102.10423/) (2021)
- [DeFiNES: Enabling Fast Exploration of the Depth-first Scheduling Space for DNN Accelerators through Analytical Modeling](https://mlsanity.com/paper/2212.05344/) (2022)
- [ZigZag: A Memory-Centric Rapid DNN Accelerator Design Space Exploration Framework](https://mlsanity.com/paper/2007.11360/) (2020)
- [DNNFuser: Generative Pre-Trained Transformer as a Generalized Mapper for Layer Fusion in DNN Accelerators](https://mlsanity.com/paper/2201.11218/) (2022)

## Abstract (arXiv)

Deploying Convolutional Neural Networks (CNNs) on edge platforms necessitates efficient hardware acceleration. Any unnecessary data movement in such accelerators can unacceptably degrade performance and efficiency. To address this, we develop a layer fusion technique targeting CNNs, that reduces off-chip data communication using a Genetic Algorithm (GA) applied to graph-based topological sort. Results show a 1.8× increase in energy efficiency and 1.9× improvement in energy-delay product (EDP) for MobileNet-v3 on a SIMBA-like mobile architecture. Our approach consistently improves workload performance, averaging 1.4× improvement to EDP for SIMBA and 1.12× for Eyeriss.