CAREER: SHF: Bio-Inspired Microsystems for Energy-Efficient Real-Time Sensing, Decision, and Adaptation

职业:SHF:用于节能实时传感、决策和适应的仿生微系统

基本信息

  • 批准号:
    2340799
  • 负责人:
  • 金额:
    $ 59.42万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2024
  • 资助国家:
    美国
  • 起止时间:
    2024-06-01 至 2029-05-31
  • 项目状态:
    未结题

项目摘要

Contemporary artificial intelligence systems typically do not adapt to their environment in real time, are very power hungry, and are typically developed in isolation from the underlying hardware. However, biological intelligence has addressed these limitations, being energy-efficient, adaptable, and well-integrated with the underlying substrate. Using biological intelligence as the guiding principle, this project develops microelectronic systems that can efficiently, sense physical signals from their environment, make real-time decisions, and adapt and learn with minimal energy usage. By drawing inspiration from biology, this project blurs the distinction between sensing, computation, and algorithm by seamless integration of memory, computing, and sensing and a learning algorithm. Through mirroring this biological model, the project seeks to develop the next generation autonomous intelligent systems with applications ranging from smarter cellphones to improved brain-machine interfaces. The outlined educational activities will enable collaboration with industrial partners and historically black colleges and universities to enable cutting edge microelectronic education to enable the domestic workforce to meet the strategic semiconductor needs of the Nation.The project co-designs continual learning algorithms with cutting-edge, energy-efficient, microelectronic designs that leverage emerging devices in the form of Ferroelectric Field-Effect Transistors (FeFETs) to enable next-generation, energy-efficient, adaptive hardware for sensing, decision-making, and learning. Analog-to-Feature converter front-end systems leveraging FeFETs as programmable transconductances will be designed to acquire analog input and extract pertinent learned features. These subsystems will feed downstream FeFET-based compute-in-memory (CIM) circuits with custom-designed circuits to alleviate the analog-to-digital converter bottleneck currently limiting most CIM architectures. Static Random Access Memory will augment FeFET structures to enable on-chip learning and dynamic reconfigurability. In lockstep with the underlying hardware, tailored continual learning algorithms will be co-designed with the analog-to-digital converters and the FeFET array to endow the system with energy-efficient resilience and adaptation. Microelectronic systems designed using the presented approach could see wide ranging applications from brain-computer-interfaces and implantable systems to blind waveform classification for wireless systems. To validate the approach, an integrated circuit will be fabricated and measured. These will also serve to provide data to further refine and calibrate software models for performance evaluation and design-space exploration. This project will ultimately develop components critical for biologically inspired, energy-efficient, autonomous agents.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.
当代人工智能系统通常不能真实的适应其环境,非常耗电,并且通常是在与底层硬件隔离的情况下开发的。然而,生物智能已经解决了这些限制,节能,适应性强,并与底层基质良好整合。该项目以生物智能为指导原则,开发微电子系统,这些系统可以有效地感知环境中的物理信号,做出实时决策,并以最小的能源消耗进行适应和学习。通过从生物学中汲取灵感,该项目通过无缝集成记忆,计算和传感以及学习算法来模糊传感,计算和算法之间的区别。通过镜像这种生物模型,该项目旨在开发下一代自主智能系统,其应用范围从更智能的手机到改进的脑机接口。概述的教育活动将使与工业合作伙伴和历史悠久的黑人学院和大学的合作,使尖端的微电子教育,使国内劳动力,以满足国家的战略半导体需求。该项目共同设计持续学习算法与尖端,节能,利用铁电场效应晶体管(FeFET)形式的新兴器件来实现下一代、节能、自适应硬件的微电子设计,和学习利用FeFET作为可编程跨导的模拟到特征转换器前端系统将被设计为采集模拟输入并提取相关的学习特征。这些子系统将为下游基于FeFET的存储器计算(CIM)电路提供定制设计的电路,以缓解目前限制大多数CIM架构的模数转换器瓶颈。静态随机存取存储器将增强FeFET结构,以实现片上学习和动态可重构性。与底层硬件同步,定制的持续学习算法将与模数转换器和FeFET阵列共同设计,以赋予系统节能的弹性和适应性。使用所提出的方法设计的微电子系统可以看到广泛的应用,从脑机接口和植入式系统的盲波形分类的无线系统。为了验证该方法,将制造和测量集成电路。这些还将提供数据,进一步完善和校准用于性能评估和设计空间探索的软件模型。该项目将最终开发出对生物启发、节能、自主代理至关重要的组件。该奖项反映了NSF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。

项目成果

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Siddharth Joshi其他文献

FDI, Poverty, and the Politics of Potable Water Access
外国直接投资、贫困和饮用水获取政治
DropOut and DropConnect for Reliable Neuromorphic Inference under Energy and Bandwidth Constraints in Network Connectivity
DropOut 和 DropConnect 在网络连接的能量和带宽约束下实现可靠​​的神经形态推理
The neurobench framework for benchmarking neuromorphic computing algorithms and systems
用于神经形态计算算法和系统基准测试的神经基准框架
  • DOI:
    10.1038/s41467-025-56739-4
  • 发表时间:
    2025-02-11
  • 期刊:
  • 影响因子:
    15.700
  • 作者:
    Jason Yik;Korneel Van den Berghe;Douwe den Blanken;Younes Bouhadjar;Maxime Fabre;Paul Hueber;Weijie Ke;Mina A. Khoei;Denis Kleyko;Noah Pacik-Nelson;Alessandro Pierro;Philipp Stratmann;Pao-Sheng Vincent Sun;Guangzhi Tang;Shenqi Wang;Biyan Zhou;Soikat Hasan Ahmed;George Vathakkattil Joseph;Benedetto Leto;Aurora Micheli;Anurag Kumar Mishra;Gregor Lenz;Tao Sun;Zergham Ahmed;Mahmoud Akl;Brian Anderson;Andreas G. Andreou;Chiara Bartolozzi;Arindam Basu;Petrut Bogdan;Sander Bohte;Sonia Buckley;Gert Cauwenberghs;Elisabetta Chicca;Federico Corradi;Guido de Croon;Andreea Danielescu;Anurag Daram;Mike Davies;Yigit Demirag;Jason Eshraghian;Tobias Fischer;Jeremy Forest;Vittorio Fra;Steve Furber;P. Michael Furlong;William Gilpin;Aditya Gilra;Hector A. Gonzalez;Giacomo Indiveri;Siddharth Joshi;Vedant Karia;Lyes Khacef;James C. Knight;Laura Kriener;Rajkumar Kubendran;Dhireesha Kudithipudi;Shih-Chii Liu;Yao-Hong Liu;Haoyuan Ma;Rajit Manohar;Josep Maria Margarit-Taulé;Christian Mayr;Konstantinos Michmizos;Dylan R. Muir;Emre Neftci;Thomas Nowotny;Fabrizio Ottati;Ayca Ozcelikkale;Priyadarshini Panda;Jongkil Park;Melika Payvand;Christian Pehle;Mihai A. Petrovici;Christoph Posch;Alpha Renner;Yulia Sandamirskaya;Clemens J. S. Schaefer;André van Schaik;Johannes Schemmel;Samuel Schmidgall;Catherine Schuman;Jae-sun Seo;Sadique Sheik;Sumit Bam Shrestha;Manolis Sifalakis;Amos Sironi;Kenneth Stewart;Matthew Stewart;Terrence C. Stewart;Jonathan Timcheck;Nergis Tömen;Gianvito Urgese;Marian Verhelst;Craig M. Vineyard;Bernhard Vogginger;Amirreza Yousefzadeh;Fatima Tuz Zohora;Charlotte Frenkel;Vijay Janapa Reddi
  • 通讯作者:
    Vijay Janapa Reddi
Comparative Analysis of Passive, Active, and Hybrid Active Filters for Power Quality Improvement in Grid-Connected Photovoltaic System
无源、有源和混合有源滤波器改善并网光伏系统电能质量的比较分析
A 0.57 mm2 Platform with 70.7% Efficient 4 mA 3.2 V Charge Pump and a Current-Input Ramp ADC for Implantable Optical Sensing of Tumors
A%200.57%20mm2%20平台%20和%2070.7%%20效率%204%20mA%203.2%20V%20充电%20泵%20和%20a%20电流输入%20斜坡%20ADC%20用于%20植入%20光学%20传感%20of%20肿瘤

Siddharth Joshi的其他文献

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{{ truncateString('Siddharth Joshi', 18)}}的其他基金

EAGER: An Analog Hardware System for Solving Boolean Satisfiability
EAGER:用于解决布尔可满足性的模拟硬件系统
  • 批准号:
    1644368
  • 财政年份:
    2016
  • 资助金额:
    $ 59.42万
  • 项目类别:
    Standard Grant

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天然超短抗菌肽Temporin-SHf衍生多肽的构效分析与抗菌机制研究
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  • 批准号:
    81572468
  • 批准年份:
    2015
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    60.0 万元
  • 项目类别:
    面上项目

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协作研究:SHF:小型:LEGAS:大规模学习演化图
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