SHF: Small: Associative Memory based on Ovenized Resonator Exchange

今天的计算系统能够以惊人的速度和效率进行处理，其基础架构是1945年约翰·冯·诺伊曼（John Von Neumann）首次提出的，当时计算机还没有出现。寻找更好的计算方法的研究人员受到了人类大脑“架构”的启发，它代表了图像处理和模式识别等应用程序的最终处理效率。有人提出尝试通过人工神经元和突触的耦合网络来模拟大脑，从而创造神经计算机和联想记忆回路。然而，这种基于传统硅技术的实现在很大程度上被认为是不切实际的，并且在复杂性和功耗方面不如传统的计算硬件。该项目将展示一种基于系统架构和纳米级氮化铝（AlN）谐振器器件共同设计的新型神经计算系统，该谐振器器件作为实现人工神经元和突触的热可调元件。这个项目的目标是演示这种系统在模式识别问题上的功能。该项目结合了两项令人兴奋的技术，即大脑功能建模和压电材料，这两项技术都引起了广泛的兴趣。压电材料，如晶体，是将机械能转化为电能的令人兴奋的示范。该项目中使用的相同材料将被纳入CMU本科课程18-220，这是一门电路入门课程，以演示能量收集和共振的重要概念。一个基于将振动能转换为电能的实验作业将鼓励学生探索收集能量的技术，以支持未来的电子系统。更简单的压电材料样品会对挤压和振动做出反应，将用于点亮led，以在卡内基梅隆大学（http://www.cmu.edu/cmites/）的C-Mite K至10级课程中进行演示。以大脑为灵感的电子系统将被纳入卡内基梅隆大学数字集成电路设计的研究生课程项目中。这项研究的结果也将与工业界的研究人员进行合作交流，特别是英特尔和高通，以探索在便携式电子系统中使用基于神经计算的联想记忆的好处。

Today's computing systems that are capable of processing at phenomenal speed and efficiency are based on an architecture that was first proposed by John Von Neumann in 1945, before computers existed. Researchers who have searched for better ways to perform computing have been inspired by the human brain "architecture" that represents the ultimate in processing efficiency for applications such as image processing and pattern recognition. Attempts to create neurocomputers and associative memory circuits that mimic the brain via a network of coupled artificial neurons and synapses have been proposed. However, such implementations based on conventional silicon technology have largely been viewed as impractical and inferior to traditional computing hardware in terms of complexity and power consumption. This project will demonstrate a novel neurocomputing system based on the co-design of the system architecture and the nanoscale aluminum nitride (AlN) resonator devices that act as thermally-tunable elements for implementation of artificial neurons and synapses. The goal of this project is to demonstrate the functionality of such a system for a pattern recognition problem.This project combines two exciting technologies, namely, modeling the function of the brain and piezoelectric materials, both of which are of interest to a broad community. Piezoelectric materials, such as crystals, are an exciting demonstration of converting mechanical energy into electrical energy. The same materials used in this project will be incorporated into the CMU undergraduate course 18-220, which is an introduction to circuits, to demonstrate energy harvesting and the important concept of resonance. A lab assignment based on converting vibrational energy to electrical energy will encourage the students to explore techniques for harvesting energy to support future electronic systems. Simpler samples of piezoelectric materials that react to being squeezed and vibrated will be used to light LEDs for demonstration to the C-Mite K through 10th classes at Carnegie Mellon (http://www.cmu.edu/cmites/ ). The brain-inspired electronic systems will be incorporated into graduate course projects for digital integrated circuit design at Carnegie Mellon. The results of this research will also be collaboratively exchanged with researchers from industry, specifically Intel and Qualcomm, to explore the benefits of neurocomputing-based associative memories for use in portable electronic systems.