人工智能芯片可能会从数据中心溢出到书桌上
Last year, the company released the DGX Station to enable software engineers to experiment with software libraries used in artificial intelligence and improve algorithms before sending them to the cloud, where the software is trained on enormous amounts of data. The workstation contains chips based on Nvidia’s Volta architecture and provides 480 trillion floating-point operations per second, or teraflops.
The DGX workstation shares the same software stack as the DGX-1 appliance, a miniature supercomputer that provides 960 teraflops of performance. That way, software engineers can swiftly swap software between Nvidia’s workstations and appliances, which can be installed in data centers where training typically happens.
Nvidia引入了两种产品，以收紧对人工智能市场的控制，并促进其Volta架构，其中包含用于处理深度学习的定制张量核心。但是，根据一位行业高管的说法，该公司的竞争对手可以使用相同的策略将其定制芯片推向软件工程师。
“They say, in the early phases of designing neural networks, we don’t want to go to data centers,” said Jin Kim, chief data science officer for machine learning chip startup Wave Computing. “We want a workstation right next to us for experimentation, taking elements of existing neural networks and putting them together like Lego blocks.”
He declined to disclose whether Wave Computing plans to release its own workstation. But the company, which has raised $117 million over the last nine years, has been putting the finishing touches on an appliance equipped with its dataflow processing unit (DPU), which supports lower precision operations that consume less power and memory than traditional chips.
When it is finished, the appliance is projected to provide performance of 2.9 quadrillion operations per second for machine learning workloads. Wave Computing has also built a special compiler that translates code into a form that its silicon can understand. The company designed its coarse-grained reconfigurable array chips to have 16,384 cores.
Kim说，Wave Computing敏锐地意识到，软件工程师正在要求工作站在数据中心之外使用算法进行实验。其他初创公司几乎可以肯定收到了相同的请求。但是还没有人冒险挑战NVIDIA。
Nvidia将其设备和工作站推向了深度学习的强大软件包。它将最初的捆绑包之一卖给了Avitas Systems，这是一家自动检查初创公司，该启动启动了软件，该软件使用水下管道和电厂炼油厂等设施中的水下管道和关键设备等事物的照片来识别腐蚀和其他缺陷。
NVIDIA的DGX Systems产品营销总监Tony Paikeday说：“您可能会有进行大量实验的研究人员或数据科学家进行了大量的实验。”“在开发生命周期的那个阶段，我们发现开发人员不愿意被数据中心的资源坐姿所困扰。他们希望它靠近他们坐着的地方。”
“We did this because we wanted to offer a proof point to the marketplace,” said Paikeday, adding that Nvidia looks forward to original equipment manufacturers selling their own workstations using its graphics chips and software language. “We wanted to set a blueprint for them to follow,” he said in an interview withElectronic Design.
其他公司也可能遵循NVIDIA的蓝图。除了波浪计算外，GraphCore和Intel都在处理可能与工作站配对的服务器设备。Groq和Cerebras Systems等初创公司可以模仿它们，以自定义硅和训练所需的大量内存构建盒子。
There are other hints. Over the last year, Graphcore has raised $110 million from investors that include Dell, the second largest supplier of workstations as well as a prolific purveyor of server infrastructure. The company claims that its custom hardware can be used to shorten the training phase of deep learning from days to hours.
Industry analysts say it is also possible that Dell will acquire Graphcore, giving it custom chips to install in its servers and gateways. Hewlett Packard, the largest supplier of workstations and another major maker of servers, is also considered a potential destination for startups like Groq and Cerebras Systems, which are still operating under the radar.
That could affect where these companies stand in the market for deep learning chipsets, which the research firm Tractica predicts could grow from $513 million in 2016 to $12.2 billion by 2025. Nvidia estimates that the market for computer chips used in training could generate $15 billion in 2020, in contrast to $11 billion for inferencing, which requires less powerful chips.
Paikeday无法讨论具体数字，但他说Nvidia几乎总是出售其工作站与设备配对。这种情况可能会改变，因为它继续用车站的功率和便携性来吸引小型公司。例如，Avitas系统将系统带到收集信息的位置，并在现场编辑算法。
Wave首席数据科学官Kim在接受采访时说：“我还认为培训将移出数据中心。”“有一些小型企业希望在本地进行早期培训，尤其是在使用转移学习之类的方法时，您不需要充满计算资源的数据中心来培训和部署合理准确的模型。”
转移学习是用于培训小型数据的培训软件的技术。它涉及删除经过特定任务的神经网络的层次，例如识别照片中的面孔，同时保留较低的网络级别，该网络较低，该网络处理更原始的任务，例如模式匹配。将较低的水平作为基础，可以对较小的数据进行培训，以完成更专业的任务，例如发现皮肤癌。
Many industries like finance and healthcare are starting to experiment with transfer learning, said Kim. And since it requires less computing power and less data than training from scratch, engineers could wing it with appliances or workstations instead of renting cloud infrastructure. That could be a faster growing market than data centers, said Kim.
本文最初发表于电子设计 -www.electronicdesign.com
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