呻吟, a startup developing artificial intelligence chips for data centers, autonomous cars, and other areas, announced the closing of a $300 million funding round, bringing its total haul to more than $365 million since it was founded in 2016.
这些资金将促进公司努力聘请人才和加快技术发展。该公司计划在2020年之前将员工人数增加到250人，以至于2020年的122人增加了122人。它还在第二代AI硅的数据中心，汽车和其他市场工作计划利用资金将其进入生产。
资金转换成最迪之一p-pocketed challengers to Nvidia, which has long dominated the AI market with its graphics processing units (GPUs) for data centers.
初创公司由前陀螺队成立于2016年。首席执行官Jonathan Ross先前共同创立了发明谷歌的张量处理单元的团队，或TPU，专门设计用于在其巨大的数据中心运行机器学习软件，它在云上租给其他人。他的联合创始人Douglas Wightman也在谷歌的“X”研究分支机构，但他不再在GRAQ工作。
“AI受到现有系统的限制，其中许多人正在遵循或逐步改善新进入者，”谷歌研发部门在谷歌研发部门，在一份声明中表示。“无论你在问题上扔多少钱，像GPUS和CPU这样的遗产架构，以跟上人工智能和机器学习的日益增长的需求。“
But instead of out-engineering the TPU or AI chips from its rivals, it started with the software and then it created a simple programmable chip designed from a clean sheet for artificial intelligence. According to Ross, the startup said it worked on the software stack and the compiler that it uses to schedule instructions in its chip for half a year before it started designing the silicon.
TSP而不是创建一个小型可编程核心并复制它数百或数千次，而是一个类似于VLIW处理器的单一核心，该处理器具有数百个功能单元和大量内存来执行AI琐事。该启动表明它可以每秒处理1000万亿兆的操作，比NVIDIA芯片更加延迟，使其更加性能。
One of the unique improvements is that it pushed most of the control and planning functions for the chip—for instance, managing memory and plotting data movement—to software. That saves valuable silicon real estate, giving it more performance in a more compact area. The company's compiler loads instructions to the correct place at the correct time in the chip, reducing the latency.
This is part of what the startup calls its "software-first mindset." The compiler choreographs the movement of instructions through the chip, resulting in fast and predictable (also known as "deterministic") performance. The software knows exactly how the chip works and how long it will take to carry out each AI computation, which is valuable in areas where safety and accuracy are paramount.
呻吟said the new architecture is specifically designed to handle AI inference chores but it can also run the types of computations used to train AI on large amounts of data. It is working with several early customers in the financial industry and national labs that run real-time workloads that require the reduced latency and massive SRAM of the single-core TSP, the company said.
呻吟has started shipping its first TSP in server accelerator cards and pre-integrated systems and the startup plans to use the funding in part to scale up production to serve its early customers.
在最初的几年里，公司主要由Chamath Palihapitiya的社会资本资助。但由于它希望扩展其客户群并将其第二代产品放入大规模生产中，该公司部分地占据了现金储备，以帮助其雇用工程人才。“我们想雇用更多的人，但我们仍然很高，”罗斯说。
多年来，Tech Giants拥有来自半导体行业的虹吸顶级工程师，但他表示，许多同一台工程师现在也希望使用硬件。“人才一直流出半导体产业进入软件。最好的人在Facebook，奥戈尔州，”罗斯说。“这些是我们招聘的公司。”
其他初创公司也对Dethrone Nvidia的GPU进行了大量的资金，目前AI培训中的当前黄金标准以及英特尔的CPU，广泛用于推理。
Graphcore, which has partnered with Dell and Microsoft, has rolled out chips with more than one thousand of its intelligence processing unit (IPU) cores that work together to carry out AI chores. The startup is valued at $2.5 billion. SambaNova Systems landed $676 million in funding this month led by SoftBank, bringing its total raised to more than $1 billion and valuing it at around $5 billion.
但尽管大量的风险资金，但大多数初创公司都努力从人群中脱颖而出。NVIDIA---与“张量核心”持续升级其通用芯片，非常适合AI培训和推理 - 和英特尔 - 这已开始销售具有集成AI加速器的服务器芯片 - 在数据中心市场深处根深蒂固。
But the increased capital and new hires will give Groq more flexibility and expertise, which is valuable as it works to roll out its second-generation chip and swipe market share from Nvidia.
The funding round was co-led by investment giant Tiger Global Management and D1 Capital, and it was supplemented by The Spruce House Partnership and Addition, the venture capital firm founded by Lee Fixel. TDK Ventures and other existing investors also invested more money in Groq.
