A Generalized Optimization Engine (GOE) for Edge AI Inference Acceleration
Artificial intelligence (AI) models have demonstrated remarkable capabilities across various domains, yet their widespread deployment is impeded by significant computational costs, particularly on resource-constrained devices. This paper explores the theoretical underpinnings of various AI model optimization techniques, algorithms, and abstractions, discussing their potential to reduce computational complexity, memory footprint, latency, and power consumption. Furthermore, we propose a comprehensive hardware (HW) and model-agnostic generalized optimization architecture that integrates these techniques for improved efficiency. Our study underscores the critical role of such a generalized o...
| Authors | Venkat R. Dasari, Jakob A. Adams, Vinod K. Mishra et al. |
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| Primary category | cs.AI |
| Published | 19 August 2026 |
| Last updated at source | 19 August 2026 |
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