SPX: Collaborative Research: Ula! - An Integrated Deep Neural Network (DNN) Acceleration Framework with Enhanced Unsupervised Learning Capability

鉴于最近无监督学习算法的革命(例如，生成性对抗性网络和双重学习)及其应用的出现，来自Duke和UCSB的三个PI/co-PI组成了一个团队来设计Ula！-一个具有增强的无监督学习能力的集成DNN加速框架。该项目从软件(算法)、硬件(计算)和应用(实现)三个方面引入了一个集成的无监督学习计算框架，从而使DNN研究发生了革命性的变化。该项目呼应了来自白宫的大脑倡议(2013)和受纳米技术启发的未来计算大挑战(2015)的号召。通过引入计算范式和人工智能(AI)之间的协同作用，研究成果将使计算智能(CI)和计算机体系结构(CA)行业普遍受益。相应的教育部分将通过引入关于人工智能软硬件协同设计的跨学科模块来加强现有的课程和教学方法，并特别关注女性和代表性不足的少数群体。该项目完成三项任务：(1)在软件层面，设计了一个广义分层决策(GHDM)系统，以有效地执行最先进的无监督学习和强化学习过程，大大降低了计算成本；(2)在硬件层面，基于近数据计算、GPU体系结构和FGPA平台的新颖性，设计了具有增强的非监督学习支持的新型DNN计算范例；(3)在应用程序层面，Ula！在可以从无监督学习和强化学习中获得极大好处的场景中被利用。所开发的技术还分别在三个有代表性的计算平台上进行了演示和评估：GPU、FPGA和新兴的纳米级计算系统。

In light of very recent revolutions of unsupervised learning algorithms (e.g., generative adversarial networks and dual-learning) and the emergence of their applications, three PIs/co-PI from Duke and UCSB form a team to design Ula! - an integrated DNN acceleration framework with enhanced unsupervised learning capability. The project revolutionizes the DNN research by introducing an integrated unsupervised learning computation framework with three vertically-integrated components from the aspects of software (algorithm), hardware (computing), and application (realization). The project echoes the call from the BRAIN Initiative (2013) and the Nanotechnology-Inspired Grand Challenge for Future Computing (2015) from the White House. The research outcomes will benefit both Computational Intelligence (CI) and Computer Architecture (CA) industries at large by introducing a synergy between computing paradigm and artificial intelligence (AI). The corresponding education components  enhance existing curricula and pedagogy by introducing interdisciplinary modules on the software/hardware co-design for AI with creative teaching practices, and give special attentions to women and underrepresented minority groups.The project performs three tasks: (1) At the software level, a generalized hierarchical decision-making (GHDM) system is designed to efficiently execute the state-of-the-art unsupervised learning and reinforcement learning processes with substantially reduced computation cost; (2) At the hardware level, a novel DNN computing paradigm is designed with enhanced unsupervised learning supports, based on the novelties in near data computing, GPU architecture, and FGPA + heterogeneous platforms; (3) At the application level, the usage of Ula! is exploited in scenarios that can greatly benefit from unsupervised learning and reinforcement learning. The developed techniques are also demonstrated and evaluated on three representative computing platforms: GPU, FPGA, and emerging nanoscale computing systems, respectively.