AI Compute Architecture and Evolution Trends

TL;DR

This paper presents a structured seven-layer model for AI compute architecture, analyzes the evolution of large language models across these layers, and discusses emerging trends and challenges in AI development.

Contribution

It introduces a novel seven-layer AI compute architecture model and applies it to analyze the evolution and technological trends of large language models.

Findings

Scale-Up and Scale-Out strategies impact computing architecture.

Two development paths for large language models are identified.

Evolution from single AI agents to ecosystems is discussed.

Abstract

The focus of AI development has shifted from academic research to practical applications. However, AI development faces numerous challenges at various levels. This article will attempt to analyze the opportunities and challenges of AI from several different perspectives using a structured approach. This article proposes a seven-layer model for AI compute architecture, including Physical Layer, Link Layer, Neural Network Layer, Context Layer, Agent Layer, Orchestrator Layer, and Application Layer, from bottom to top. It also explains the three stages in the evolution of large language models (LLMs) using the proposed 7-layer model. For each layer, we describe the development trajectory and key technologies. In Layers 1 and 2 we discuss AI computing issues and the impact of Scale-Up and Scale-Out strategies on computing architecture. In Layer 3 we explore two different development paths for LLMs. In Layer 4 we discuss the impact of contextual memory on LLMs and compares it to traditional processor memory. In Layers 5 to 7 we discuss the trends of AI agents and explore the issues in evolution from a single AI agent to an AI-based ecosystem, and their impact on the AI industry.