2 research outputs found
Dynamic Early Exiting Predictive Coding Neural Networks
Internet of Things (IoT) sensors are nowadays heavily utilized in various
real-world applications ranging from wearables to smart buildings passing by
agrotechnology and health monitoring. With the huge amounts of data generated
by these tiny devices, Deep Learning (DL) models have been extensively used to
enhance them with intelligent processing. However, with the urge for smaller
and more accurate devices, DL models became too heavy to deploy. It is thus
necessary to incorporate the hardware's limited resources in the design
process. Therefore, inspired by the human brain known for its efficiency and
low power consumption, we propose a shallow bidirectional network based on
predictive coding theory and dynamic early exiting for halting further
computations when a performance threshold is surpassed. We achieve comparable
accuracy to VGG-16 in image classification on CIFAR-10 with fewer parameters
and less computational complexity
Dynamic nsNet2: Efficient Deep Noise Suppression with Early Exiting
Although deep learning has made strides in the field of deep noise
suppression, leveraging deep architectures on resource-constrained devices
still proved challenging. Therefore, we present an early-exiting model based on
nsNet2 that provides several levels of accuracy and resource savings by halting
computations at different stages. Moreover, we adapt the original architecture
by splitting the information flow to take into account the injected dynamism.
We show the trade-offs between performance and computational complexity based
on established metrics.Comment: Accepted at the MLSP 202