Future Computing Hardware for AI
Future Computing Hardware for AI
复制标题
人工智能的未来计算硬件
DOI：
10.1109/iedm.2018.8614482
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
C. Goldberg
中科院分区：
文献类型：
--
作者：
J. Welser;J. Pitera;C. Goldberg
硬件在狭窄的AI的成熟和扩散中发挥了支持作用，但将发挥领导作用来实现广泛AI的创新和采用。广泛AI与专用硬件的并发演变将改变云与边缘，结构化和非结构化数据之间的传统平衡，以及培训和推理。在各种计算资源（包括高带宽CPU，专业的AI加速器）以及每个节点中都注入了高性能网络，以产生重大的性能改善，因此已经交付了异质系统架构。展望未来，我们设想了一个专业技术的路线图，以加速AI，从异质数字数字von Neumann机器开始，探索降低精确的加速器方法，找到使用Analog AI设备进行常规设备功能的限制，并通过量子计算完成。对于AI。
Hardware has taken on a supporting role in the maturation and proliferation of narrow AI, but will take a leading role to enable the innovation and adoption of broad AI. The concurrent evolution of broad AI with purpose-built hardware will shift traditional balances between cloud and edge, structured and unstructured data, and training and inference. Heterogeneous system architectures are already being delivered where varied compute resources, including high-bandwidth CPUs, specialized AI accelerators, and high-performance networking are infused in each node to yield significant performance improvements. Looking to the future, we envision a roadmap of specialized technologies to accelerate AI, starting with heterogeneous digital von Neumann machines, exploring reduced-precision accelerator approaches, finding the limits of conventional device power-performance with analog AI devices, and finishing with quantum computing for AI.