适用于 AI 工作负载的不可知软件将软件与硬件解耦.pdf

1、Agnostic Software for AI WorkloadsDecoupling Software from Hardware2Change is ComingAdapt or3Workloads are Evolving at an Accelerated PaceModels are increasing in size exponentially,with no foreseeable slow downNetwork architectures are changing rapidlyTransformers are not the only solution that wil

2、l existThe balance between training and inference is 50/50,even at scaleThe progression from ML to AI to Agents is not slowing downInsight:Observations:4Machine Learning is the Gateway to Autonomous AgentsSoftware 1.0Rule based and deterministic1957Software 2.0Data driven and discriminative2011Softw

3、are 3.0Data driven and generative2018Software 4.0Goal-Directed agents2024Software 5.0Semi-Autonomous agents2030Software 6.0General Purpose agents20365Can Hardware Architecture Evolution Keep Up?Industry is hungry for a GPU alternative that has better efficiency Weve reached the limit of physics for

4、improved efficiency Even at max capacity,Fabs cannot supply enough hardware,GPU or otherwise Physical build out of data centers can only give 10%of the capacity we need Novel hardware architectures could help,but software takes a decade to catch upTheres enough capacity in todays compute architectur

5、es if we could maximize utilization.Insight:Observations:6If Not Specialized Hardware,Then What?What We Have Build out is insufficient compute and cost prohibitive Models gain adoption based on how well they map to GPUs Increased reliance on GPU leaves increasingly unused compute elsewhere Barrier t

6、o adopt any alternatives to GPUs are high What We Need Heterogeneous compute is necessary to meet the compute demand of AI Automated performance optimization on heterogenous clusters Architectures and compilers designed in the context of a cluster unitGAP=6 Orders of Magnitude*By 20327Can Software T