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

该项目通过生物系统的两个关键见解来解决电子芯片设计中更高性能和更好能效的问题：非布尔信息编码（大脑中的模拟处理）和共定位记忆和计算（如大脑突触）。该项目的具体目标是创建一个具有内在非二进制表示和内存内存和计算的有效信息处理的设计框架。如果成功，该项目可以阐明信息编码及其在确定系统能源效率方面的物理实现的基本作用，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。除了对科技的影响外，该项目的综合教育计划是为不同背景的学生提供跨学科的经验，并培养一个具有社会意识的终身学习者社区。该项目将在包括内存计算（IMC）和近传感器处理（NSP）在内的广泛应用中以无缝的方式联合优化电路、架构和算法，并由三个主要研究重点组成：(1)推进AMS设计自动化，开发新颖的神经网络启发的模型抽象和硬件基板，以实现使用AMS电路作为信息处理构建块的简化设计流程；(2)为了支持灵活高效的内存计算架构，本项目将利用先前开发的AMS设计方法，构建智能和可延展的外设接口和编译框架；(3)为了解决资源受限传感器系统中的能源效率挑战，它将通过开发具有多信号通道和多种传感模式的高效近传感器处理来探索上下文感知的模拟-信息前端设计。这些将作为理解异构系统中整体交互和设计权衡性能、效率、安全性和安全性的构建块。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.