CAREER: Neural Network-Inspired Information Processing Beyond the Binary Digital Abstraction

该项目探讨了电子芯片设计中更高性能和更高能效的问题，其中包括来自生物系统的两个关键见解：非布尔信息编码（大脑中的模拟处理），以及共同定位的记忆和计算（如大脑突触）。该项目的具体目标是创建一个设计框架，用于使用内在的非二进制表示和内存内存和计算进行高效的信息处理。如果成功，该项目可以揭示信息编码及其物理实现在确定系统能效方面的基本作用，并提供实用的设计自动化方法，在数字化步骤之前将计算和学习注入模拟/混合信号（AMS）领域。除了技术影响外，该项目的综合教育计划还旨在为来自不同背景的学生提供跨学科的经验，并培养具有社会意识的终身学习者社区。该项目将以无缝的方式在广泛的应用中实现电路，架构和算法的联合优化，包括内存计算（IMC）和近传感器处理（NSP），包括三个主要研究方向：（1）为促进AMS设计自动化，将开发新颖的神经网络启发的模型抽象和硬件基板，以实现简化的设计流程，使用AMS电路作为信息处理的构建块;（2）为了支持灵活和高效的存储器内计算架构，本项目将利用早期开发的AMS设计方法，构建智能和可延展的外围接口和编译框架;（3）为解决资源有限的传感器系统的能源效率挑战，将通过开发具有多个信号通道和多个传感模式的高效近传感器处理，探索上下文感知的模拟到信息前端设计。这些将成为理解异构系统中性能、效率、安全性和安全性的整体交互和设计权衡的基石。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。

This project approaches the question of higher performance and better energy efficiency in electronic chip design with two key insights from biological systems: non-Boolean information encoding (analog processing in brain), and co-localized memory and computation (as in brain synapses). The specific objective of this project is to create a design framework for efficient information processing with intrinsic non-binary representations and in-memory memory and computation. If successful, this project can shed light on the fundamental role of information encoding and its physical implementation in determining system energy efficiency, as well as provide practical design automation methodology to infuse computation and learning into the analog/mixed-signal (AMS) domain before the digitalization step. Apart from its technological impacts, the integrated educational plan of this project is to empower students from all backgrounds with interdisciplinary experience and to cultivate a community of lifelong learners with social awareness.The project will enable joint optimization of circuit, architecture, and algorithm in a seamless manner across wide-range of applications including in-memory computing (IMC) and near-sensor processing (NSP), and consists of three major research thrusts: (1) to advance AMS design automation, novel neural network-inspired model abstraction, and hardware substrate will be developed to enable a streamlined design flow that uses AMS circuits as building blocks for information processing; (2) to support flexible and efficient in-memory computing architecture, this project will build intelligent and malleable peripheral interfaces and compilation framework by leveraging the AMS design methodology developed earlier; (3) to address the energy efficiency challenge in resource-constrained sensor systems, it will explore a context-aware analog-to-information frontend design by developing efficient near-sensor processing with multiple signal channels and multiple sensing modalities. These will serve as building blocks towards understanding the holistic interactions and design trade-offs of performance, efficiency, safety, and security in heterogeneous systems.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.