Recently, with the advent of the Internet of everything and 5G network, the amount of data generated by various edge scenarios such as autonomous vehicles, smart industry, 4K/8K, virtual reality (VR), augmented reality (AR), etc., has greatly exploded. All these trends significantly brought real-time, hardware dependence, low power consumption, and security requirements to the facilities, and rapidly popularized edge computing. Meanwhile, artificial intelligence (AI) workloads also changed the computing paradigm from cloud services to mobile applications dramatically. Different from wide deployment and sufficient study of AI in the cloud or mobile platforms, AI workload performance and their resource impact on edges have not been well understood yet. There lacks an in-depth analysis and comparison of their advantages, limitations, performance, and resource consumptions in an edge environment. In this paper, we perform a comprehensive study of representative AI workloads on edge platforms. We first conduct a summary of modern edge hardware and popular AI workloads. Then we quantitatively evaluate three categories (i.e., classification, image-to-image, and segmentation) of the most popular and widely used AI applications in realistic edge environments based on Raspberry Pi, Nvidia TX2, etc. We find that interaction between hardware and neural network models incurs non-negligible impact and overhead on AI workloads at edges. Our experiments show that performance variation and difference in resource footprint limit availability of certain types of workloads and their algorithms for edge platforms, and users need to select appropriate workload, model, and algorithm based on requirements and characteristics of edge environments.
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Real-Time Physical Threat Detection on Edge Data Using Online Learning
Sensor-powered devices offer safe global connections; cloud scalability and flexibility, and new business value driven by data. The constraints that have historically obstructed major innovations in technology can be addressed by advancements in Artificial Intelligence (AI) and Machine Learning (ML), cloud, quantum computing, and the ubiquitous availability of data. Edge AI (Edge Artificial Intelligence) refers to the deployment of AI applications on the edge device near the data source rather than in a cloud computing environment. Although edge data has been utilized to make inferences in real-time through predictive models, real-time machine learning has not yet been fully adopted. Real-time machine learning utilizes real-time data to learn on the go, which helps in faster and more accurate real-time predictions and eliminates the need to store data eradicating privacy issues. In this article, we present the practical prospect of developing a physical threat detection system using real-time edge data from security cameras/sensors to improve the accuracy, efficiency, reliability, security, and privacy of the real-time inference model.
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- Award ID(s):
- 2039583
- NSF-PAR ID:
- 10447286
- Date Published:
- Journal Name:
- IEEE Consumer Electronics Magazine
- ISSN:
- 2162-2248
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The edge computing paradigm has recently drawn significant attention from industry and academia. Due to the advantages in quality-of-service metrics, namely, latency, bandwidth, energy efficiency, privacy, and security, deploying artificial intelligence (AI) models at the network edge has attracted widespread interest. Edge-AI has seen applications in diverse domains that involve large amounts of data. However, poor dataset quality plagues this compute regime owing to numerous data corruption sources, including missing data. As such systems are increasingly being deployed in mission-critical applications, mitigating the effects of corrupted data becomes important. In this work, we propose a strategy based on data imputation using neural inversion, DINI. It trains a surrogate model and runs data imputation in an interleaved fashion. Unlike previous works, DINI is a model-agnostic framework applicable to diverse deep learning architectures. DINI outperforms state-of-the-art methods by at least 10.7% in average imputation error. Applying DINI to mission-critical applications can increase prediction accuracy to up to 99% (F1 score of 0.99), resulting in significant gains compared to baseline methods.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/MCE.2023.3256641
