Machine Learning Accelerators in 2.5D Chiplet Platforms with Silicon Photonics

2023/01/28 by Febin Sunny, Ebadollah Taheri, Sunny, Febin +5
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Hardware Architecture (cs.AR) #Machine Learning (cs.LG) #Neural Networks and Reservoir Computing #Optical Network Technologies #Photonic and Optical Devices

paper · pdf · doi:10.48550/arxiv.2301.12252

openalex publication_date 2023/01/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Domain-specific machine learning (ML) accelerators such as Google's TPU and Apple's Neural Engine now dominate CPUs and GPUs for energy-efficient ML processing. However, the evolution of electronic accelerators is facing fundamental limits due to the limited computation density of monolithic processing chips and the reliance on slow metallic interconnects. In this paper, we present a vision of how optical computation and communication can be integrated into 2.5D chiplet platforms to drive an entirely new class of sustainable and scalable ML hardware accelerators. We describe how cross-layer design and fabrication of optical devices, circuits, and architectures, and hardware/software codesign can help design efficient photonics-based 2.5D chiplet platforms to accelerate emerging ML workloads.

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