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SHF: Small: A Unified Approach for Scheduling Computer Vision Dataflow Graphs

SHF：小型：调度计算机视觉数据流图的统一方法

基本信息

批准号：
1910748
负责人：
Jason Bakos
金额：
$ 24.94万
依托单位：
University of South Carolina at Columbia
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910748&HistoricalAwards=false
关键词：
SHF Small Unified Approach Scheduling

项目摘要

Advances in computer vision and machine learning are enabling continuous development of new capabilities in autonomous systems and knowledge extraction from images and video. These advances are possible in large part to increased computational capability brought about by a new class of processor designed for visual and neural processing. This includes Google's Visual Core and Tensor Processor Unit, Qualcomm's Hexagon DSP, Nvidia's Programmable Vision Accelerator and Tensor Core, and Intel's Crest processors. In these processors, the program code exerts far greater control of the underlying processing and memory resources than in general purpose and graphical processors. This serves to greatly reduce the uncertainties that would otherwise restrict accurate performance prediction and open the door for new approaches for automatic and portable performance optimization. Despite the growing market for embedded vision processors, their associated compiler tools are nascent, relying on hand-tuning program code using trial-and-error methods. This project will build upon existing technologies to build a "universal" front-end for a broad class of vision processors. As part of this project the researchers will also develop an database of OpenVX 1.2 kernels (https://www.khronos.org/openvx/) and an OpenVX benchmark suite. It is expected that developing both the tools and benchmarks in the open source will facilitate community involvement. The artifacts developed in this project will serve as a pedagogical infrastructure for a new course in Internet-of-Things and machine learning at the edge of the computer network.This project is developing a new program compilation flow, in which a program written in the platform-independent OpenVX representation is converted into a low-level representation and explicitly scheduled onto the underlying hardware resources. The conversion is governed by a mathematical model of the hardware behavior trained from mining performance data generated from executing and instrumenting a series of benchmarks. The compiler framework developed using this approach has the potential to replace the ad hoc methods currently in use and become a powerful tool for developing software for visual and neural processors, which will power a future generation of cognizant smart devices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

计算机视觉和机器学习的进步使自主系统的新功能以及从图像和视频中提取知识的能力不断发展。这些进步在很大程度上可能是由于为视觉和神经处理而设计的一类新处理器所带来的计算能力的增加。其中包括谷歌的Visual Core和Tensor处理器单元、高通的Hexagon DSP、Nvidia的可编程视觉加速器和Tensor Core以及英特尔的Crest处理器。在这些处理器中，程序代码对底层处理和存储器资源施加比通用和图形处理器更大的控制。这有助于大大减少不确定性，否则会限制准确的性能预测，并为自动和便携式性能优化的新方法打开大门。尽管嵌入式视觉处理器的市场不断增长，但其相关的编译器工具仍处于萌芽状态，依赖于使用试错法手动调整程序代码。该项目将建立在现有技术的基础上，为广泛的视觉处理器构建一个“通用”前端。作为该项目的一部分，研究人员还将开发OpenVX 1.2内核数据库（https：//www.khronos.org/openvx/）和OpenVX基准套件。预计开发开源工具和基准将促进社区参与。该项目开发的工件将作为物联网和计算机网络边缘机器学习新课程的教学基础设施。该项目正在开发一种新的程序编译流程，其中以平台无关的OpenVX表示编写的程序将转换为低级表示并显式调度到底层硬件资源上。转换由硬件行为的数学模型控制，该模型通过挖掘执行和检测一系列基准测试所生成的性能数据来训练。使用这种方法开发的编译器框架有可能取代目前使用的临时方法，并成为开发视觉和神经处理器软件的强大工具，这将为下一代认知智能设备提供动力。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。