CSR: Small: Energy-efficient Embedded Signal-processing Inference Systems

机器学习算法能够从过于复杂而无法进行分析建模的数据中进行模式识别。这种模式识别在不同的领域具有根本的重要性。这些算法正在成为嵌入式系统的一个重要组成部分，发现在基础设施，环境监测，个人健康监测，能源管理，食品供应链，装配线等，这项研究有可能使这些系统的重大进展，使高能效的传感器上进行推理。由于计划让来自代表性不足的群体的学生参与进来，工业参与，向更广泛的公众推广，以及在线分发工具，预计将产生广泛的影响。所提出的工作的目的是探索通过随机投影嵌入式信号处理推理系统实现的节能。先前已经在压缩感测的上下文中采用随机投影来降低系统能量。我们已经发现，当使用随机投影来压缩奈奎斯特信号时，压缩机制更加鲁棒，同时提供了两个数量级的系统能量节省的可能性。 我们称这种机制为压缩信号处理。 我们建议通过新的方法和信号处理架构来实现这一概念。此外，我们建议使用遗传规划和错误感知推理来解决非线性信号处理问题。 我们计划广泛的系统级的能量精度的权衡所提出的机制提供的评估。

Machine-learning algorithms enable pattern recognition from data that are too complex to model analytically. This pattern recognition is of fundamental importance in diverse domains. These algorithms are becoming an essential part of embedded systems that find use in infrastructure, environmental monitoring, personal health monitoring, energy management, food supply chain, assembly lines, etc. This research has the potential to enable significant advances in such systems by enabling highly energy-efficient on-sensor inference to be performed. With its plans for involving students from underrepresented groups, industrial engagement, outreach to the broader public, and online distribution of tools, it is expected to have a broad impact. The aim of the proposed work is to explore the energy savings achievable by embedded signal-processing inference systems through random projections. Random projections have previously been employed in the context of compressive sensing to reduce system energy. We have found that when random projections are used to compress Nyquist signals, the compression mechanism is far more robust, while offering the possibility of two orders of magnitude system energy savings. We term this mechanism compressed signal processing. We propose work on bringing this concept to fruition through new methodologies and signal-processing architectures. In addition, we propose the use of genetic programming and error-aware inference to tackle the nonlinear signal-processing problem. We plan extensive evaluations of the system-level energy-accuracy tradeoffs the proposed mechanisms offer.