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

职业：超越二进制数字抽象的神经网络启发信息处理

基本信息

批准号：
1942900
负责人：
Xuan Zhang
金额：
$ 50万
依托单位：
Washington University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942900&HistoricalAwards=false
关键词：
CAREER Neural Network Inspired Information

项目摘要

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.

该项目通过生物系统的两个关键见解：非树状信息编码（大脑中的模拟处理）以及共定位的内存和计算（如大脑突触中），从而解决了电子芯片设计中高性能和更好的能源效率的问题。该项目的具体目标是创建一个设计框架，以使用内在的非二进制表示以及内存内存和计算进行有效的信息处理。如果成功，该项目可以阐明信息编码的基本作用及其在确定系统能效中的物理实施的基本作用，并提供实用的设计自动化方法，以在数字化步骤之前将计算和学习注入模拟/混合信号（AMS）域中。 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）将开发以新型神经网络启发的模型抽象和硬件基板的发展，以启用简化的设计流，该流程使用AMS电路作为信息处理；（2）为了支持灵活有效的内存计算体系结构，该项目将通过利用早期开发的AMS设计方法来构建智能且可延展的外围界面和编译框架；（3）为了应对资源受限的传感器系统中的能源效率挑战，它将通过开发有效的近传感器处理和多种信号通道和多个感应方式来探索上下文感知的模拟信息前端设计。这些将成为理解异质系统绩效，效率，安全和安全性的整体互动和设计权衡的基础。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛影响的审查标准通过评估来获得支持的。

项目成果

期刊论文数量（16）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Neural-PIM: Efficient Processing-In-Memory With Neural Approximation of Peripherals

DOI：
10.1109/tc.2021.3122905
发表时间：
2022-01
期刊：
IEEE Transactions on Computers
影响因子：
3.7
作者：
Weidong Cao;Yilong Zhao;Adith Boloor;Yinhe Han;Xuan Zhang;Li Jiang
通讯作者：
Weidong Cao;Yilong Zhao;Adith Boloor;Yinhe Han;Xuan Zhang;Li Jiang

LeCA: In-Sensor Learned Compressive Acquisition for Efficient Machine Vision on the Edge

DOI：
10.1145/3579371.3589089
发表时间：
2023-06
期刊：
Proceedings of the 50th Annual International Symposium on Computer Architecture
影响因子：
0
作者：
Tianrui Ma;Adith Boloor;Xiangxing Yang;Weidong Cao;Patrick Williams;Nan Sun;Ayan Chakrabarti;
通讯作者：
Tianrui Ma;Adith Boloor;Xiangxing Yang;Weidong Cao;Patrick Williams;Nan Sun;Ayan Chakrabarti;

Hercules: Heterogeneity-Aware Inference Serving for At-Scale Personalized Recommendation

DOI：
10.48550/arxiv.2203.07424
发表时间：
2022-03
期刊：
2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA)
影响因子：
0
作者：
Liu Ke;Udit Gupta;Mark Hempstead;Carole-Jean Wu;Hsien-Hsin S. Lee;Xuan Zhang
通讯作者：
Liu Ke;Udit Gupta;Mark Hempstead;Carole-Jean Wu;Hsien-Hsin S. Lee;Xuan Zhang

RoSE: Robust Analog Circuit Parameter Optimization with Sampling-Efficient Reinforcement Learning