Designing High-Performance and Thermally Feasible Multi-Chiplet Architectures enabled by Non-bendable Glass Interposer

TL;DR

This paper presents a co-optimization framework for multi-chiplet architectures with glass interposers, addressing warpage challenges to improve performance, reduce power, and ensure reliability in large-scale systems.

Contribution

It introduces a novel design framework that combines architecture and packaging optimization to mitigate warpage and enhance performance in glass interposer-based multi-chiplet systems.

Findings

Achieves up to 64.7% performance improvement.

Reduces power consumption by 40%.

Enables scalable, reliable multi-chiplet architectures.

Abstract

Multi-chiplet architectures enabled by glass interposer offer superior electrical performance, enable higher bus widths due to reduced crosstalk, and have lower capacitance in the redistribution layer than current silicon interposer-based systems. These advantages result in lower energy per bit, higher communication frequencies, and extended interconnect range. However, deformation of the package (warpage) in glass interposer-based systems becomes a critical challenge as system size increases, leading to severe mechanical stress and reliability concerns. Beyond a certain size, conventional packaging techniques fail to manage warpage effectively, necessitating new approaches to mitigate warpage induced bending with scalable performance for glass interposer based multi-chiplet systems. To address these inter-twined challenges, we propose a thermal-, warpage-, and performance-aware design framework that employs architecture and packaging co-optimization. The proposed framework disintegrates the surface and embedded chiplets to balance conflicting design objectives, ensuring optimal trade-offs between performance, power, and structural reliability. Our experiments demonstrate that optimized multi-chiplet architectures from our design framework achieve up to 64.7% performance improvement and 40% power reduction compared to traditional 2.5D systems to execute deep neural network workloads with lower fabrication costs.