SHF: Core: Small: Real-time and Energy-Efficient Machine Learning for Robotics Applications

SHF：核心：小型：用于机器人应用的实时且节能的机器学习

基本信息

批准号：
2128036
负责人：
Ruth Bahar
金额：
$ 50万
依托单位：
Brown University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2128036&HistoricalAwards=false
关键词：
SHF Core Small Real time

项目摘要

Technological advancements have led to a proliferation of robots using machine learning to assist humans in a wide range of tasks. However, despite the strengths of approaches based on deep learning they have several shortcomings that leave them vulnerable to exploitation from adversaries. In addition, the computational, financial, and environmental cost incurred to train these discriminative models can be quite immense. Alternatively, hybrid methods that combine (less complex) deep learning with other probabilistic techniques can provide more robust and adaptive learning. Unfortunately, these probabilistic techniques tend to suffer from long run times and high computational complexity. This project aims to develop new approaches for hardware acceleration of these probabilistic techniques across a range of robotics applications. These approaches are intended to pave the way for the design of autonomous robots that can sense, perceive, and act in real time in a range of natural human environments, and in a very energy-efficient manner. The proposed work has the potential to enhance human quality-of-life by enabling robots to dramatically expand the range of tasks they can complete. The project also includes curriculum development for an interdisciplinary course in robotic design aimed in part at getting a broader range of students interested in computing and hardware design of robotic systems.A long-term goal is to reach the point where mobile robots can compute all information needed for perception on-board and in real time. This project focuses on exploiting the complementary properties of deep learning and probabilistic inference for making perceptual decisions, where the weaknesses of one can be addressed by the strengths of the other. The researchers are investigating various algorithmic and hardware-acceleration approaches that provide effective robot perception in unstructured, natural environments in real time and at efficient energy cost. In particular, the research is aimed at goal-directed robot manipulation within a confined embedded system under limited hardware and power budgets. The focus is on probabilistic algorithms such as Bayesian inference that may be incorporated with neural network methods. The project proposes three main tasks: a) accelerating graph-based Bayesian inference in hardware, b) constructing a general-purpose library of optimized hardware modules for accelerating robot-oriented algorithms, and c) using the hardware library to develop new algorithms for robot perception.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.

技术进步导致机器人大量使用机器学习来协助人类完成各种任务。然而，尽管基于深度学习的方法具有优势，但它们也存在一些缺点，使它们很容易受到对手的利用。此外，训练这些判别模型所需的计算、财务和环境成本可能相当巨大。或者，将（不太复杂的）深度学习与其他概率技术相结合的混合方法可以提供更稳健和自适应的学习。不幸的是，这些概率技术往往面临运行时间长和计算复杂度高的问题。该项目旨在开发新方法，在一系列机器人应用中对这些概率技术进行硬件加速。这些方法旨在为自主机器人的设计铺平道路，这些机器人可以在一系列自然人类环境中以非常节能的方式实时感知、感知和行动。拟议的工作有可能通过使机器人大幅扩展其可以完成的任务范围来提高人类的生活质量。该项目还包括机器人设计跨学科课程的课程开发，部分目的是让更多的学生对机器人系统的计算和硬件设计感兴趣。长期目标是达到移动机器人可以计算所有信息的程度船上实时感知所需。该项目的重点是利用深度学习和概率推理的互补特性来做出感知决策，其中一个的弱点可以通过另一个的优势来解决。研究人员正在研究各种算法和硬件加速方法，这些方法可以在非结构化的自然环境中以高效的能源成本实时提供有效的机器人感知。特别是，该研究旨在在有限的硬件和功率预算下，在有限的嵌入式系统中进行目标导向的机器人操作。重点是概率算法，例如可以与神经网络方法结合的贝叶斯推理。该项目提出了三个主要任务：a）在硬件中加速基于图的贝叶斯推理，b）构建优化硬件模块的通用库以加速面向机器人的算法，以及c）使用硬件库开发机器人的新算法该奖项反映了 NSF 的法定使命，并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，被认为值得支持。