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什么样的“芯片”能支撑起智能时代?
2017-08-18 11:04:58 中国经济网
“如果用刀来比喻芯片,通用处理器好比一把瑞士军刀,人工智能时代好比要拿刀来切肉,瑞士军刀可以拿来用,但它并非是为切肉设计的,所以效果并非最好。因此,我们需要专门打造一把切肉的刀,这把刀既要方便切肉,又要方便剁骨头,还需要具有一定的通用性。” 国内人工智能芯片领军企业中科寒武纪创始人陈天石这样描述人工智能芯片的重要性。高性能计算是人工智能发展的基石,也是最重要的基础设施。个人电脑时代和移动互联网时代的发展历程表明,承载高性能计算的芯片决定了一个新的计算平台的基础架构和发展生态。因此,业内将芯片视为人工智能时代的战略制高点和入场券。
目前,除了英特尔和高通等传统芯片企业加紧研发外,谷歌等全球科技巨头也纷纷加入芯片研发行列,中国处于奋力追赶状态,在不同技术路线上均有所突破。不过受限于芯片产业短板,我国人工智能芯片发展仍然任重道远。
“芯片不突破,不可能真正成功”
国内最早聚焦和从事人工智能软硬件结合与芯片研究的地平线机器人技术创始人兼CEO余凯表示,人工智能时代,上层的应用都依赖于底层核心能力,而这个核心能力就是人工智能处理器。清华大学微电子学研究所所长魏少军甚至直言:“中国这么多优秀的科学家正在做各种各样的人工智能算法和应用,但是如果在芯片上不能突破,就不可能真正成功。”
陈天石介绍,信息时代的产物——通用处理器(CPU),无法支撑起智能时代。例如,谷歌大脑用了上万个通用处理器核“跑”了数天来学习如何识别猫脸;“阿尔法狗”和韩国棋手李世石下棋时使用了上千个CPU和数百个图形处理器(GPU),平均每局电费近3000美元。对于绝大多数智能需求来说,基于通用处理器的传统计算机成本过高、速度过慢、功耗过高、体积过大,难以接受。
目前,国际科技巨头已经开始抢占这个人工智能时代的战略制高点。传统的CPU主要用来完成单线程任务,而人工智能目前采用的深度学习算法,有海量的数据运算需求,主要需要并行运算,对传统架构和系统提出了极大的挑战,AI芯片由此应运而生。2011年,仍在谷歌就职的吴恩达将英伟达的GPU应用于谷歌大脑中取得惊人效果,结果表明12颗GPU可以提供相当于2000颗CPU的深度学习性能。之后各地人工智能研究人员纷纷使用GPU来加速AI算法,GPU也成为目前最主流的AI芯片。根据英伟达公开信息,短短两年里,与英伟达在深度学习方面展开合作的企业便激增了近35倍,增至3400多家企业。去年,谷歌推出了其自主芯片TPU,并成功帮助升级版的“阿尔法狗”战胜中国围棋选手柯洁,它与GPU相比,用较低的精度提高性能,功耗下降到GPU的10%。老牌芯片企业英特尔也通过收购以色列公司Nervana布局人工智能芯片。
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