Improvements in Interlayer Pipelining of CNN Accelerators Using Genetic Algorithms
Not on the map. Why?
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.
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 7 of its 17 references that are in the map. First posted on arXiv 20 Nov 2023.
Builds on
7 of 17 references.
References · 7
- 2015Deep Residual Learning for Image Recognitioncited by 51,950
- 2019Searching for MobileNetV3cited by 1,990
- 2022TinyViT: Fast Pretraining Distillation for Small Vision Transformerscited by 232
- 2021An Evaluation of Edge TPU Accelerators for Convolutional Neural Networkscited by 39
- 2022DeFiNES: Enabling Fast Exploration of the Depth-first Scheduling Space for DNN Accelerators through Analytical Modelingcited by 12
- 2020ZigZag: A Memory-Centric Rapid DNN Accelerator Design Space Exploration Frameworkcited by 6
- 2022DNNFuser: Generative Pre-Trained Transformer as a Generalized Mapper for Layer Fusion in DNN Acceleratorscited by 2
Built on by
Not cited yet.