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Real Time On-Die Power & Thermal Profiling for Machine Learning Design Applications

Thomas To (Technical Director, Xilinix)

Ajay Kumar Sharma (Senior Manager, Xilinx)

Nitin Srivastava (Staff SI Engineer, Xilinx)

Location: Ballroom B

Date: Thursday, January 30

Time: 2:50pm - 3:30pm

Track: 01. Signal & Power Integrity for Single-Multi Die, Interposer & Packaging, 05. Advanced I/O Interface Design for Memory & 2.5D/3D/SiP Integrations

Format: Technical Session

Pass Type: 2-Day Pass, All-Access Pass, Alumni All-Access Pass - Get your pass now!

Vault Recording: TBD

Audience Level: All

Many Advanced Computing System-on-Chip (SOC) implement special computing elements such as Artificial Intelligent Engine (AIE) to support AI Machine Learning (ML) Applications. Because ML applications are still evolving, the power and thermal conditions of the ML computing SOC is not clearly understood. This paper will show a System Monitor (Sys Mon) that can monitor the on-die conditions when different ML applications are used when pairing up with DDR4 and LPDDR4 system memory. The monitored voltage will be compared to direct voltage probing results. The knowledge obtained can be applied to optimize between system requirements and physical system conditions.

Takeaway

Understanding of the real time on-die condition is critical especially for advanced computing platform such as in machine learning applications. This paper presents the system monitor to facilitate real time on-die measurement. Different usage comparisons allow system designers to trade off between system requirement and physical conditions.