Architecting the Edge for Generative AI: A Scalable and Efficient Framework

• Author(s): Gokul Chandra Purnachandra Reddy
• Paper ID: 1707228
• Page: 776-792
• Published Date: 31-10-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 4 October-2024

Abstract

Next-generation Generative Artificial Intelligence (GenAI) models are?evolving with unprecedented pace, bringing new opportunities but also challenges for computing architectures such as scalability, performance, and computational efficiency. Although traditional cloud-based platforms, which are powerful, have?great limitations to support real-time GenAI applications. These limitations?arise from latency, bandwidth, and security constraints, which have made cloud-based solutions less suitable for resource-intensive AI workloads, especially relevant for applications requiring real-time inference with low latency. In particular, LLMs and GANs are definitely complex and computationally expensive, requiring tons of processing power,?memory and storage, and real-time inferable features. Moreover, with the continuous growth of the scale?and sophistication of GenAI models, traditional cloud computing challenges are becoming ever-present for meeting the needs of the set of distributed systems, especially for applications that depend on instant responses. The requirements for the size of data needed for training and inference tasks compounds upon this?limitation. One exciting option to solve this issue comes from decentralizing the computation?and leveraging the power of edge computing. It reduces the load on the cloud by bringing the AI training and inference processes closer?to the data sources. It's about using attachable and?typically mobile devices—Internet of Things (IoT) sensors, smartphones and even dedicated, standalone devices—to process and analyze data without having to move it out. This distributed approach offers many benefits to GenAI applications, especially lowering latency, bandwidth requirements, and?time-to-response.

Keywords

Edge Computing, Generative AI, Federated Learning, Model Compression, Neuromorphic Computing, Cloud-Edge Hybrid, Privacy-Preserving AI

Citations

IRE Journals:
Gokul Chandra Purnachandra Reddy "Architecting the Edge for Generative AI: A Scalable and Efficient Framework" Iconic Research And Engineering Journals Volume 8 Issue 4 2024 Page 776-792

IEEE:
Gokul Chandra Purnachandra Reddy "Architecting the Edge for Generative AI: A Scalable and Efficient Framework" Iconic Research And Engineering Journals, 8(4)