XPS: DSD: Collaborative Research: NeoNexus: The Next-generation Information Processing System across Digital and Neuromorphic Computing Domains

“大数据”应用的爆炸式增长对传统计算机系统的数据处理速度和可扩展性提出了严峻的挑战。传统的冯诺依曼机的性能受到CPU和存储器之间性能差距的极大阻碍，激发了对新的或替代的计算架构的积极研究。通过模仿大脑的自然大规模并行架构，紧密耦合的存储器和计算以及独特的模拟域操作，神经形态计算系统有望为图像识别和自然语言理解等应用提供上级速度。本研究的目的是建立NeoNexus的基本框架和设计方法。新一代信息处理系统的灵感来自于人类大脑皮层。它通过在忆阻器交叉阵列上利用基于大规模推理的数据处理和计算加速技术，将神经形态计算加速器与传统计算资源集成在一起。计算和数据交换将得到创新的互连架构的精心协调和支持，即，分层片上网络（NoC）。将开发软硬件协同设计平台，以应对各种设计挑战。该项目将帮助计算机体系结构和高性能计算社区克服传统体系结构日益增长的技术挑战，加速传统计算技术与认知计算模型之间的融合。它还将促进人工智能技术在现代计算机体系结构中的应用，并推动软件和硬件层面的发明。参与这项研究的本科生和研究生将接受下一代半导体行业劳动力的培训。

The explosion of "big data" applications imposes severe challenges of data processing speed and scalability on traditional computer systems. The performance of traditional Von Neumann machines is greatly hindered by the increasing performance gap between CPU and memory, motivating the active research on new or alternative computing architectures. By imitating brain's naturally massive parallel architecture with closely coupled memory and computing as well as the unique analog domain operations, neuromorphic computing systems are anticipated to deliver superior speed for applications in image recognition and natural language understanding.The objective of this research is to establish the fundamental framework and design methodology for NeoNexus -- the next-generation information processing system inspired by human neocortex. It integrates neuromorphic computing accelerators with conventional computing resources by leveraging large scale inference-based data processing and computing acceleration technique atop memristor crossbar arrays. The computation and data exchange will be carefully coordinated and supported by the innovative interconnect architecture, i.e., a hierarchical network-on-chip (NoC). The software-hardware co-design platform will be developed to address the various design challenges. The project will help computer architecture and high-performance computing communities to overcome the ever-increasing technical challenges of traditional architectures and accelerate the fusion between conventional computing technology and cognitive computing model. It will also promote the applications of artificial intelligence technology advances in modern computer architectures and motivate the inventions at both software and hardware levels. Undergraduate and graduate students involved in this research will be trained for the next-generation semiconductor industry workforce.