Next-Generation AI Training: Hybrid Computing Models, Quantum Accelerators, and Energy-Efficient Architectures
Year: 2024
Type: article
Abstract: Artificial Intelligence (AI) has rapidly advanced in recent years, with models growing exponentially in scale, complexity, and capability. However, these advancements have led to significant computational and energy demands during training and inference. This paper explores the latest innovations in AI training efficiency, focusing on hybrid computing models, quantum accelerators, and specialized energy-efficient hardware architectures. It highlights state-of-the-art models, including DeepSeek-V3, and emerging technologies like Google’s Willow quantum processor and EnCharge AI's specialized chips. A comparative analysis is presented, evaluating each method's scalability, training efficiency, and energy consumption. The paper concludes by proposing a hybrid training framework that integrates classical AI hardware, quantum computing, and algorithmic optimization for scalable and sustainable AI training.
Author: BIN KHALID, AHMED
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6930414133
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