Download or read book Resource Constrained Neural Architecture Design written by Yunyang Xiong and published by . This book was released on 2021 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep neural networks have been highly effective for a wide range of applications in computer vision, natural language processing, speech recognition, medical imaging, and biology. Large amounts of annotated data, dedicated deep learning computing hardware such as the NVIDIA GPU and Google TPU, and the innovative neural network architectures and algorithms have all contributed to rapid advances over the last decade. Despite the foregoing improvements, the ever-growing amount of compute and data resources needed for training neural networks (whose sizes are growing quickly) as well as a need for deploying these models on embedded devices call for designing deep neural networks under various types of resource constraints. For example, low latency and real-time response of deep neural networks can be critical for various applications. While the complexity of deep neural networks can be reduced by model compression, different applications with diverse resource constraints pose unique challenges for neural network architecture design. For instance, each type of device has its own hardware idiosyncrasies and requires different deep architectures to achieve the best accuracy-efficiency trade-off. Consequently, designing neural networks that are adaptive and scalable to applications with diverse resource requirements is not trivial. We need methods that are capable of addressing different application-specific challenges paying attention to: (1) problem type (e.g., classification, object detection, sentence prediction), (2) resource challenges (e.g., strict inference compute, memory, and latency constraint, limited training computational resources, small sample sizes in scientific/biomedical problems). In this dissertation, we describe algorithms that facilitate neural architecture design while effectively addressing application- and domain-specific resource challenges. For diverse application domains, we study neural architecture design strategies respecting different resource needs ranging from test time efficiency to training efficiency and sample efficiency. We show the effectiveness of these ideas for learning with smaller datasets as well as enabling the deployment of deep learning systems on embedded devices with limited computational resources which may enable reducing the environmental effects of using such models.