Increasing Energy Efficiency of Server Cooling Over Traditional Methods with a Deep Reinforcement Learning Agents running on an OCP Compliant BMC platforms.pdf

1、AI-ML model for dynamic server fans speed control achieves better energy efficiency than the traditional fans control methods.Model runs on an ML engine of a BMC chip.Increasing Energy Efficiency of Server Cooling Over Traditional Methods with a Deep Reinforcement Learning Agents Running on an OCP C

2、ompliant BMC PlatformsRaghu Kondapalli,Chief Technology Officer,Axiado CorporationSundaram Arumugasundaram,Principal Security Architect,Axiado CorporationZhichao Zhang,Principal Machine Learning Architect,Axiado CorporationIncreasing Energy Efficiency of Server Cooling Over Traditional Methods with

3、a Deep Reinforcement Learning Agents running on an OCP Compliant BMC platformsDC SustainabilitySUSTAINABILITYTCU chip consists of below components:1.App processors:cores for running apps like BMC,host vulnerability management,extended detection and Response(XDR)agents2.programmable AI engine to run

4、ML models like server thermal management3.Smart-NIC for control/management plane like BMC traffic4.hardware Root of trust(HRoT)and TPM(Trusted Platform Module)to enhance server securityAxiado offers Smart-SCM that is compliant with the Open Compute Project(OCP)datacenter-ready secure control module(

5、DC-SCM)standard.Trusted Control/Compute Unit(TCU)OverviewAI-Powered Dynamic Thermal Management(DTM)from BMC:BMC is ideal for server thermal management due to its existing role in various server management functions,including power control.Faster Thermal Prediction and Calibration:TCU collects sensor

6、 data directly,bypassing the host OS,enabling faster thermal prediction and fan speed calibration.Rich Dataset for Decision Making:As an OCP DC-SCM compliant BMC,TCU gathers comprehensive data from all chassis components(CPUs,GPUs,etc.)via diverse connections(I2C,eSPI,USB,PCI-e),providing a rich dat