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利用硅光子技术开发人工智能加速器 - Sang Yoon Han(DGIST).pdf

上传人: 拾亿 编号:751724 2025-07-29 33页 2.76MB

1、Han 1Development of AI AcceleratorsLeveragingSilicon Photonics TechnologySangyoon Han,DGIST2025/June/15thHan 2Silicon Photonics AI Transfer Learning EngineSangyoon Han,DGIST2025/June/15thHan 3HAETAE project overviewjoongang.co.kr Haetae Beast in Korean folklore Cow+horse+lion with horn A creature de

2、feating fireHan 4Goal:Multi-Material-Chiplet Photonic Transfer Learning Engine Toward the realization of optical computings ultimate vision By Chiplets(Si photonics)Heterogeneous integration(Multi-material)Near-zero static power MEMSHan 5The team 2 from Korea,3 from EUGreeceBelgiumGermanyKoreaKoreaP

3、rof.Gunther Roelkens Prof.Kyoungsik YuDr.Leonardo Del BinoProf.Nikolaos PlerosProf.Sangyoon HanHan 6Optical computingHan 7Calculation using electrons Complexity emerges from simplicityhttp:/hyperphysics.phy-astr.gsu.edu/hbase/Electronic/nand.htmlhttps:/ 8Calculation using light Simple Han 9Calculati

4、on using light1023201020.Han 10Calculation using light =4 2x4 Lens1011122 Han 11Matrix computation using lens.10135.571.28.31.45411.=.Han 12Matrix computation using lensX.Lin et al.,Science 2018Han 13Paradigm Shift:Computing with Light Fundamental breakthrough possiblePhysics-based optimization Leas

5、t-Time,Least-Energy Nature-Driven OptimizationParallelismSpatial,modal,and wavelength divisionMulti-dimensional paradigmUltra-BroadbandCarrier frequency:100 THzWavelength multiplexing:Dissipationless Lossless transmission of information Even better at higher bandwidthLow latency Calculation through

6、propagation of light Latency=distance/cQuantum nature of light Intrinsic Robustness to Thermal FluctuationsMcMahon,P.L.The physics of optical computing.Nat Rev Phys 5,717734(2023).Han 14Leveraging Silicon PhotonicsHan 15Photonic Integrated Circuits(PICs)“Photonics on a chip”Integrated with electroni

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本文主要介绍了利用硅光子技术发展AI加速器的HAETAE项目。关键点如下: 1. 项目目标:开发多材料芯片级光子传输学习引擎,实现光学计算的最终愿景。 2. 技术优势:基于光计算具有低功耗、低延迟、高并行性和超宽带特性,可实现高达10^17 MAC/s的计算速度。 3. 光子集成电路(PIC):实现“光子芯片”,与电子集成,提升性能、密度、速度和成本效益。 4. 技术挑战:PIC存在大静态功耗、小非线性、慢学习率等问题。 5. 解决方案:采用电机械相移器、添加III/V材料、多芯片级技术等手段克服上述挑战。 6. 应用前景:适用于低功耗、低延迟的应用场景。 核心数据引用:光子矩阵-向量乘法可实现约10^17 MAC/s的计算速度,而商用GPU约为10^13至10^15 MAC/s。PIC的静态功耗已达100W/cm2,与电饭煲相当。
"硅光子学如何革新AI加速?" "光学计算的优势有哪些?" "如何克服光子集成电路的挑战?"
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