This paper explores the environmental impact of the super-linear growth trends for AI from a
holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the …

Green artificial intelligence (AI) is more environmentally friendly and inclusive than
conventional AI, as it not only produces accurate results without increasing the …

Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive
safely. Given the limited hardware resources, existing 3D perception models are not able to …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

The advancements in machine learning (ML) opened a new opportunity to bring intelligence
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …

We revisit the one-shot Neural Architecture Search (NAS) paradigm and analyze its
advantages over existing NAS approaches. Existing one-shot method, however, is hard to …

Abstract Differentiable Neural Architecture Search (DNAS) has demonstrated great success
in designing state-of-the-art, efficient neural networks. However, DARTS-based DNAS's …

One of the most critical problems in weight-sharing neural architecture search is the
evaluation of candidate models within a predefined search space. In practice, a one-shot …

Recently, there has been a growing interest in automating the process of neural architecture
design, and the Differentiable Architecture Search (DARTS) method makes the process …

Neural architecture search (NAS) has shown promising results discovering models that are
both accurate and fast. For NAS, training a one-shot model has become a popular strategy …