Presentation Type
Article
Location
Kennesaw, Georgia
Start Date
1-4-2026 12:30 PM
End Date
1-4-2026 1:45 PM
Description
As generative artificial intelligence and multi-agent systems transition from experimental research prototypes into the backbone of enterprise-critical infrastructure, the industry has reached a pivotal juncture where computing bottlenecks represent the primary barrier to progress. The historical reliance on "brute-force" scaling, simply increasing model parameter counts, has reached a point of diminishing returns, as the exponential escalation in training costs and energy consumption becomes economically and environmentally unsustainable. To address these challenges, this paper provides a comprehensive examination of six cutting-edge technological pillars currently redefining neural architecture and optimization: Neural Architecture Search (NAS) for automated design, Advanced efficient training techniques, Energy-efficient Neuromorphic computing, Quantum Machine Learning (QML), Hardware-software co-design, and the latest methodologies in Model compression and pruning. For each of these six pillars, the study presents verified, empirical results derived from peer-reviewed research, highlighting the specific techniques that are currently reshaping the landscape of enterprise AI deployment. Beyond identifying successes, the paper meticulously documents the technical and physical constraints that currently limit these technologies. Finally, we integrate the recently proposed AgentOS framework (arXiv:2602.20934, Feb. 24, 2026) as a unifying, OS-level abstraction. We demonstrate how AgentOS serves as the essential connective tissue between these six pillars, providing a standardized architectural layer that manages hardware resources and algorithmic efficiency to support the next generation of scalable, autonomous agentic systems.
Optimization, Co-Design and NextGen of Agentic AI
Kennesaw, Georgia
As generative artificial intelligence and multi-agent systems transition from experimental research prototypes into the backbone of enterprise-critical infrastructure, the industry has reached a pivotal juncture where computing bottlenecks represent the primary barrier to progress. The historical reliance on "brute-force" scaling, simply increasing model parameter counts, has reached a point of diminishing returns, as the exponential escalation in training costs and energy consumption becomes economically and environmentally unsustainable. To address these challenges, this paper provides a comprehensive examination of six cutting-edge technological pillars currently redefining neural architecture and optimization: Neural Architecture Search (NAS) for automated design, Advanced efficient training techniques, Energy-efficient Neuromorphic computing, Quantum Machine Learning (QML), Hardware-software co-design, and the latest methodologies in Model compression and pruning. For each of these six pillars, the study presents verified, empirical results derived from peer-reviewed research, highlighting the specific techniques that are currently reshaping the landscape of enterprise AI deployment. Beyond identifying successes, the paper meticulously documents the technical and physical constraints that currently limit these technologies. Finally, we integrate the recently proposed AgentOS framework (arXiv:2602.20934, Feb. 24, 2026) as a unifying, OS-level abstraction. We demonstrate how AgentOS serves as the essential connective tissue between these six pillars, providing a standardized architectural layer that manages hardware resources and algorithmic efficiency to support the next generation of scalable, autonomous agentic systems.
