Improvements in Interlayer Pipelining of CNN Accelerators Using Genetic
  Algorithms
Mark Horeni Siddharth Joshi
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.