INFERENCE OPTIMIZATION IN AI SYSTEMS BASED ON TPU AND LPU ARCHITECTURES
Keywords:
Domain-specific architecture, Tensor processing unit , Language processing unit, inference, systolic array, deterministic scheduling, energy efficiency, Roofline model.Abstract
This article investigates the methods for increasing the efficiency of the inference stage in modern artificial intelligence systems, particularly in Large Language Models and deep neural networks. The paper provides a comparative analysis of the hardware and software hierarchy of two distinct Domain-Specific Architectures designed to overcome the limitations of general-purpose graphics processors: Google's Tensor Processing Unit and Groq's Language Processing Unit. The impact of Systolic Arrays and Software-Defined Deterministic Scheduling mechanisms on inference speed, latency, and energy efficiency is substantiated using mathematical models. Simulation and benchmark results indicate that the Language processing unit architecture reduces latency in sequential token generation by up to 8–10 times compared to graphics processors platforms, while the Tensor processing unit delivers higher throughput and energy savings (up to 26.8%) in parallel matrix computations.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
