基于随机阻变存储器的深度极值点学习机用于统一视觉处理
Random resistive memory-based deep extreme point learning machine for unified visual processing
AI总结：
针对边缘多模态视觉处理的异构数据、硬件能效瓶颈与模型训练成本高的问题，提出基于随机阻变存储器的软硬件协同DEPLM，实现高效统一点集分析，大幅提升能效、降低训练成本。
摘要：
包括3D LiDAR（激光雷达）、神经形态DVS传感器和传统帧相机在内的视觉传感器正越来越多地被集成到边缘智能机器中。直接在边缘智能机器上实现密集的多感官数据分析，对增强现实、虚拟现实、无人机等众多新兴边缘应用至关重要，这要求统一的数据表示、极高的硬件能效以及快速的模型训练。然而，多感官数据本质上具有异构性，给边缘智能机器的系统开发带来了极大的复杂度。此外，传统数字硬件的性能受限于处理单元与存储单元物理分离的冯·诺依曼瓶颈，以及晶体管微缩的物理极限——后者导致摩尔定律放缓。而模型规模不断扩大带来的繁琐训练进一步加剧了这些限制。我们提出了一种全新的软硬件协同设计方案：基于随机阻变存储器的深度极值点学习机（DEPLM），可实现高效的统一点集分析。我们展示了该系统在多种数据模态和两种不同学习任务上的通用性。与基于传统数字硬件的系统相比，我们的协同设计系统实现了能效的大幅提升以及训练成本的显著降低。这款基于随机阻变存储器的深度极值点学习机有望为跨多种数据模态与任务的高能效、易训练的边缘AI铺平道路。
英文摘要：
Visual sensors, including 3D LiDAR, neuromorphic DVS sensors, and conventional frame cameras, are increasingly integrated into edge-side intelligent machines. Realizing intensive multi-sensory data analysis directly on edge intelligent machines is crucial for numerous emerging edge applications, such as augmented and virtual reality and unmanned aerial vehicles, which necessitates unified data representation, unprecedented hardware energy efficiency and rapid model training. However, multi-sensory data are intrinsically heterogeneous, causing significant complexity in the system development for edge-side intelligent machines. In addition, the performance of conventional digital hardware is limited by the physically separated processing and memory units, known as the von Neumann bottleneck, and the physical limit of transistor scaling, which contributes to the slowdown of Moore's law. These limitations are further intensified by the tedious training of models with ever-increasing sizes. We propose a novel hardware-software co-design, random resistive memory-based deep extreme point learning machine (DEPLM), that offers efficient unified point set analysis. We show the system's versatility across various data modalities and two different learning tasks. Compared to a conventional digital hardware-based system, our co-design system achieves huge energy efficiency improvements and training cost reduction when compared to conventional systems. Our random resistive memory-based deep extreme point learning machine may pave the way for energy-efficient and training-friendly edge AI across various data modalities and tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
