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Silicon Photonic Chiplets with In-memory Computing Accelerate LLM Inference, Surpassing H100 Efficiency
Nov. 17, 2025 –
By Quantum Zeitgeist
Large language models demand ever-increasing computational resources, and current systems struggle with the communication bottlenecks that limit performance. Yue Jiet Chong, Yimin Wang, and Zhen Wu, alongside Xuanyao Fong from the National University of Singapore, present a novel architecture that tackles this challenge with a 3D-stacked chiplet design. Their system integrates non-volatile in-memory computing with a silicon photonic network, dramatically accelerating the processing of large language models. The team demonstrates significant speed and efficiency gains over existing technologies like the A100 and H100, and importantly, their design allows for further scalability to accommodate even more complex models, representing a substantial step forward in artificial intelligence hardware.
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