  This paper proposes an optimization of an existing Deep Neural Network (DNN)
that improves its hardware utilization and facilitates on-device training for
resource-constrained edge environments. We implement efficient parameter
reduction strategies on Xception that shrink the model size without sacrificing
accuracy, thus decreasing memory utilization during training. We evaluate our
model in two experiments: Caltech-101 image classification and PCB defect
detection and compare its performance against the original Xception and
lightweight models, EfficientNetV2B1 and MobileNetV2. The results of the
Caltech-101 image classification show that our model has a better test accuracy
(76.21%) than Xception (75.89%), uses less memory on average (847.9MB) than
Xception (874.6MB), and has faster training and inference times. The
lightweight models overfit with EfficientNetV2B1 having a 30.52% test accuracy
and MobileNetV2 having a 58.11% test accuracy. Both lightweight models have
better memory usage than our model and Xception. On the PCB defect detection,
our model has the best test accuracy (90.30%), compared to Xception (88.10%),
EfficientNetV2B1 (55.25%), and MobileNetV2 (50.50%). MobileNetV2 has the least
average memory usage (849.4MB), followed by our model (865.8MB), then
EfficientNetV2B1 (874.8MB), and Xception has the highest (893.6MB). We further
experiment with pre-trained weights and observe that memory usage decreases
thereby showing the benefits of transfer learning. A Pareto analysis of the
models' performance shows that our optimized model architecture satisfies
accuracy and low memory utilization objectives.
