  This paper introduces a Scalable Hierarchical Aware Convolutional Neural
Network (SHA-CNN) model architecture for Edge AI applications. The proposed
hierarchical CNN model is meticulously crafted to strike a balance between
computational efficiency and accuracy, addressing the challenges posed by
resource-constrained edge devices. SHA-CNN demonstrates its efficacy by
achieving accuracy comparable to state-of-the-art hierarchical models while
outperforming baseline models in accuracy metrics. The key innovation lies in
the model's hierarchical awareness, enabling it to discern and prioritize
relevant features at multiple levels of abstraction. The proposed architecture
classifies data in a hierarchical manner, facilitating a nuanced understanding
of complex features within the datasets. Moreover, SHA-CNN exhibits a
remarkable capacity for scalability, allowing for the seamless incorporation of
new classes. This flexibility is particularly advantageous in dynamic
environments where the model needs to adapt to evolving datasets and
accommodate additional classes without the need for extensive retraining.
Testing has been conducted on the PYNQ Z2 FPGA board to validate the proposed
model. The results achieved an accuracy of 99.34%, 83.35%, and 63.66% for
MNIST, CIFAR-10, and CIFAR-100 datasets, respectively. For CIFAR-100, our
proposed architecture performs hierarchical classification with 10% reduced
computation while compromising only 0.7% accuracy with the state-of-the-art.
The adaptability of SHA-CNN to FPGA architecture underscores its potential for
deployment in edge devices, where computational resources are limited. The
SHA-CNN framework thus emerges as a promising advancement in the intersection
of hierarchical CNNs, scalability, and FPGA-based Edge AI.
