Improvements in Interlayer Pipelining of CNN Accelerators Using Genetic Algorithms
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$\times$ increase in energy efficiency and 1.9$\times$ improvement in energy-delay product (EDP) for MobileNet-v3 on a SIMBA-like mobile architecture. Our approach consistently improves workload performance, averaging 1.4$\times$ improvement to EDP for SIMBA and 1.12$\times$ for Eyeriss.
PDF AbstractTasks
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
