Tutorial

Training-Free Approaches for Edge AI: Challenges, Opportunities and Progress

Radu Marculescu

Room 204
[ Abstract ]
Wed 31 Aug 1 p.m. PDT — 5 p.m. PDT

Abstract:

With the explosion in Big Data, it is often forgotten that much of the data nowadays is generated at the edge. Specifically, a major source of data is users’ endpoint devices like phones, smart watches, etc., that are connected to the internet, also known as the Internet-of-Things (IoT). Despite the huge success of deep learning (DL) in many areas (e.g., computer vision, natural language processing, etc.), the size and the computational complexity of the existing state-of-the art deep models limit the deployment of DL on resource-constrained devices and its large-scale adoption in EdgeAI. Neural architecture search (NAS) (also called AutoML) techniques have been proposed to automatically design neural architectures with reduced model sizes. The networks obtained via NAS have higher prediction accuracy and significantly fewer parameters than the hand-crafted networks. However, adapting existing NAS approaches to different hardware architectures is challenging due to their intensive computation and execution time requirements.

To address such issues, in this tutorial, we focus on the newest and perhaps the most promising breed of NAS for EdgeAI, namely approaches that are training-free and thus eminently suited for large-scale development. In particular, we plan to address a few relevant questions: What kind of system architectures can meet the AI algorithm requirements, while maximizing the prediction accuracy, inference speed as well as energy efficiency? Can we use network science or deep learning theory to understand what kind of network architectures can achieve good performance without training individual models? Can we develop efficient approaches that enable co- optimization of deep neural network accuracy and performance on real hardware platforms?

Starting from these overarching ideas, in this tutorial, we will cover both algorithmic and hardware- aware aspects of training-free model design for EdgeAI, show state-of-the-art results for relevant edge applications, and illustrate potential implications on real edge devices.

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