昆仑:面向多样化工作负载的14nm高性能AI处理器

Jian Ouyang, Xueliang Du, Yin Ma, Jiaqiang Liu
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引用次数: 8

摘要

为了能够处理语音、图像、语言、自动驾驶等广泛的人工智能应用,人工智能加速器必须具有足够的灵活性,以处理多样化的工作负载。百度自行设计的AI芯片“百度昆仑”实现了这一功能,具有很高的可编程性、灵活性和性能。百度昆仑的灵感来源于XPU架构[1]。该芯片采用三星14nm制程技术。900MHz时的峰值性能为230TOPS@INT8, 1.1GHz时的峰值性能为281TOPS@INT8。内存带宽512GB/s,峰值功率160W。百度昆仑在各种类型的工作负载下都实现了良好的性能。在900MHz基频下,BERT、ResNet50、YOLOv3的延迟分别比Nvidia T4 GPU低1.7倍、1.2倍和2倍,并使用TensorRT进行优化。最近,百度昆仑已部署在百度的数据中心,为众多应用提供服务。与Nvidia T4相比,它在搜索引擎内的几个模型中实现了1.5到3倍的性能提升。
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Kunlun: A 14nm High-Performance AI Processor for Diversified Workloads
In order to be able to handle a wide range of AI applications, such as for speech, image, language and autonomous driving, it is necessary that an AI accelerator be flexible enough to handle diversified workloads. Baidu Kunlun, an AI chip designed in-house by Baidu, achieves this capability with high programmability, flexibility and performance. Baidu Kunlun was inspired by the XPU architecture [1]. The chip is implemented in Samsung 14nm process technology. Its peak performance is 230TOPS@INT8 at 900MHz and up to 281TOPS@INT8 at 1.1GHz boost frequency. The memory bandwidth is 512GB/s and the peak power is 160W. Baidu Kunlun achieves good performance across various types of workloads. With 900MHz base frequency, the latencies of BERT, ResNet50, YOLOv3 are $1.7 \times, 1.2 \times$ and $2 \times$ less than an Nvidia T4 GPU, respectively, with optimizations from TensorRT. Recently, Baidu Kunlun has been deployed in data centers in Baidu to serve many applications. It achieves 1.5-to$- 3 \times$ better performance for several models within the search engine vs. the Nvidia T4.
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