Ultrafast Reconfigurable Topological Photonic Processing Accelerator
Wenfeng Zhou, Xin Wang, Xun Zhang, Yuqi Chen, Min Sun, Jingchi Li, Xiong Ni, Yahui Zhu, Qingqing Han, Jungan Wang, Chen Yang, Bin Li, Feng Qiu, Yikai Su, Yong Zhang

TL;DR
This paper presents a wafer-scale topological photonic chip that enables ultrafast, energy-efficient, and reconfigurable AI processing with unprecedented computational density and throughput, surpassing existing photonic platforms.
Contribution
Introduction of a topological photonic computing chip utilizing ferroelectric PZT films for ultra-fast, reconfigurable AI processing with high density and throughput.
Findings
Achieves 266 trillion operations/sec/mm² density
Provides 1.92 TOPS throughput with high accuracy
Enables zero-static-power reconfiguration
Abstract
The rise of artificial intelligence has triggered exponential growth in data volume, demanding rapid and efficient processing. High-speed, energy-efficient, and parallel-scalable computing hardware is thus increasingly critical. We demonstrate a wafer-scale non-volatile topological photonic computing chip using topological modulators. Leveraging the GHz-speed electro-optic response and nonvolatility of ferroelectric lead zirconate titanate (PZT) thin films via topological photonic confinement, Our chip enables thousand-fold faster reconfiguration, zero-static-power operation, and a computational density of 266 trillion operations per second per square millimeter . This density surpasses that of silicon photonic reconfigurable computing chips by two orders of magnitude and thin-film lithium niobate platforms by four orders of magnitude. A 16-channel wavelength-space multiplexed chip…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Photonic and Optical Devices · Topological Materials and Phenomena
