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90年代的黄河路
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在人工智能领域对高效算力的不断追求中,光计算作为一种新兴技术正崭露头角。近日,牛津大学Harish Bhaskaran院士课题组在《Nature》正刊上发表论文,揭示了部分相干光如何增强并行光计算,为光计算的商业化应用开辟了新的道路。

论文的第一作者董博维博士指出,降低光学相干性可以提升光子卷积处理的并行性,无需牺牲太多准确性。这种方法减少了对精确相位控制的需求,简化了光计算系统的复杂性,为实现大规模光子张量核提供了可能。通过部分相干光源,光计算芯片在处理速度和能效比方面展现出显著优势。

在实际应用中,研究人员使用相变材料光子存储器和9×3硅光子张量核,对帕金森病患者的步态进行了分类,准确率达到了92.2%。此外,他们还展示了一个高速光子处理器,用于MNIST手写数字数据集的分类,准确率同样高达92.4%。

光计算芯片因其高并行度、高能效比和高速度的特性,被认为在应对人工智能不断增长的算力需求方面具有巨大潜力。随着牛津大学的这项最新研究,光计算时代似乎正在加速到来。2022年,该课题组的部分成员在国内创立了光本位科技,其128*128矩阵规模的光计算芯片已达到商用标准,董博维博士的加入将进一步推动光子存算在人工智能领域的商业化进程。

然而,大规模光计算芯片的调控成本一直是个挑战,需要精确控制激光光源的波长和相位。此次研究打破了这一传统观念,表明降低光源的相干性可以降低系统复杂性,提高计算性能,为解决这一难题提供了新思路。

光计算芯片的崛起对于人工智能的未来发展,尤其是大模型训练、自动驾驶和具身智能等领域,具有深远影响。它降低了硬件成本,提高了能效,为解决这些领域算力需求与能耗之间的矛盾提供了可能的解决方案。随着技术的不断进步,光计算有望成为推动人工智能新变革的关键力量。

【source】https://www.jiqizhixin.com/articles/2024-08-28

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