Collaborative Research: CNS Core: Small: NV-RGRA: Non-Volatile Nano-Second Right-Grained Reconfigurable Architecture for Data-Intensive Machine Learning and Graph Computing

合作研究:CNS 核心:小型:NV-RGRA:用于数据密集型机器学习和图计算的非易失性纳秒右粒度可重构架构

基本信息

  • 批准号:
    2228239
  • 负责人:
  • 金额:
    $ 30.93万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-10-01 至 2025-09-30
  • 项目状态:
    未结题

项目摘要

In the era of digital data, computing devices amass vast amounts of data continuously over time. This leads to a paradigm shift in the computing by adopting machine learning (ML) and graph analytic processing to analyze such massive amounts of data. Traditional computing paradigms are inefficient in terms of energy consumption, latency, and computational efficiency. Existing in-memory computing paradigms address this challenge to a certain extent, but are not adaptable for heterogeneous applications. The proposed project introduces a novel computing architecture, right-grained reconfigurable architecture (RGRA) that combines the flexibility of coarse-grained reconfigurable array (CGRA) and programmability of FPGAs, deploying a circuit-switched interconnects and router network with torus topology to address this challenge. At the top-level, RGRA is a many-core architecture with each core configurable at finer granularity. The proposed research is also expected to lead to the development of new branch of reconfigurable architectures that support efficient execution of data-intensive applications such as graph analytics and benefit from architectural aspects such as reconfigurability and heterogeneity. In terms of broader impact, design of hardware accelerators is one of the driving directions in the field of computer architecture. As such, the successful design of RGRA that is performance efficient irrespective of the application memory-traits can have a significant societal and economic impact. For instance, it can augment CPUs, FPGAs, and GPUs in the existing and emerging systems. The results of the project will include design of high-speed reconfigurable NVMs and interconnects, which can also be adopted in many-core systems towards developing high-throughput processors. With ML being taught in the higher-secondary schools, the project has a good scope for outreach to the community and attract students, especially in terms of (i) recruitment of underrepresented classes including minorities and women; (ii) outreach in the form of K-12 and undergraduate student involvement in research via summer internships and senior-design projects; and (iii) offering a graduate course on ML accelerator design.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.
在数字数据时代,计算设备随着时间的推移不断积累大量数据。通过采用机器学习(ML)和图形分析处理来分析如此大量的数据,这导致了计算领域的范式转变。传统的计算范式在能耗、延迟和计算效率方面效率低下。现有的内存计算范式在一定程度上解决了这一挑战,但不适合异构应用程序。提出的项目引入了一种新的计算架构,右粒度可重构架构(RGRA),它结合了粗粒度可重构阵列(CGRA)的灵活性和fpga的可编程性,部署了电路交换互连和具有环面拓扑的路由器网络来应对这一挑战。在顶层,RGRA是一个多核体系结构,每个核心都可以以更细的粒度进行配置。所提出的研究也有望导致可重构架构的新分支的发展,以支持数据密集型应用程序(如图形分析)的有效执行,并从可重构性和异构性等架构方面受益。就更广泛的影响而言,硬件加速器的设计是计算机体系结构领域的驱动方向之一。因此,无论应用程序内存特征如何,RGRA的成功设计都是性能高效的,可以产生重大的社会和经济影响。例如,它可以增强现有和新兴系统中的cpu、fpga和gpu。该项目的成果将包括高速可重构nvm和互连的设计,这也可以在多核系统中采用,以开发高吞吐量处理器。由于机器学习在高中授课,该项目有很好的扩展社区和吸引学生的空间,特别是在(i)招募代表性不足的班级,包括少数民族和妇女;(ii)通过暑期实习和高级设计项目,以K-12和本科生参与研究的形式进行推广;(iii)提供机器学习加速器设计的研究生课程。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。

项目成果

期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Reconfigurable FET Approximate Computing-based Accelerator for Deep Learning Applications
  • DOI:
    10.1109/iscas46773.2023.10181758
  • 发表时间:
    2023-05
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Raghul Saravanan;Sathwika Bavikadi;Shubham Rai;Akash Kumar;Sai Manoj Pudukotai Dinakarrao
  • 通讯作者:
    Raghul Saravanan;Sathwika Bavikadi;Shubham Rai;Akash Kumar;Sai Manoj Pudukotai Dinakarrao
FlutPIM:: A Look-up Table-based Processing in Memory Architecture with Floating-point Computation Support for Deep Learning Applications
FlutPIM:: 内存架构中基于查找表的处理,支持深度学习应用的浮点计算
  • DOI:
    10.1145/3583781.3590313
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Sutradhar, Purab Ranjan;Bavikadi, Sathwika;Indovina, Mark;Pudukotai Dinakarrao, Sai Manoj;Ganguly, Amlan
  • 通讯作者:
    Ganguly, Amlan
Coarse-Grained High-speed Reconfigurable Array-based Approximate Accelerator for Deep Learning Applications
适用于深度学习应用的粗粒度高速可重构阵列近似加速器
Heterogeneous Multi-Functional Look-Up-Table-based Processing-in-Memory Architecture for Deep Learning Acceleration
3DL-PIM: A Look-up Table oriented Programmable Processing in Memory Architecture based on the 3-D Stacked Memory for Data-Intensive Applications
3DL-PIM:基于 3D 堆栈存储器的存储器架构中面向查找表的可编程处理,适用于数据密集型应用
  • DOI:
    10.1109/tetc.2023.3293140
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    5.9
  • 作者:
    Sutradhar, Purab Ranjan;Bavikadi, Sathwika;Dinakarrao, Sai Manoj;Indovina, Mark A.;Ganguly, Amlan
  • 通讯作者:
    Ganguly, Amlan
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Sai Manoj Pudukotai Dinakarrao其他文献

Address Obfuscation to Protect against Hardware Trojans in Network-on-Chips
通过地址混淆来防御片上网络中的硬件木马
Memristors' Potential for Multi-bit Storage and Pattern Learning
忆阻器在多位存储和模式学习方面的潜力
Comprehensive Analysis of Consistency and Robustness of Machine Learning Models in Malware Detection
恶意软件检测中机器学习模型的一致性和鲁棒性综合分析
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Sreenitha Kasarapu;Dipkamal Bhusal;Nidhi Rastogi;Sai Manoj Pudukotai Dinakarrao
  • 通讯作者:
    Sai Manoj Pudukotai Dinakarrao
Unified Testing and Security Framework for Wireless Network-on-Chip Enabled Multi-Core Chips
支持无线片上网络的多核芯片的统一测试和安全框架
Generative AI-Based Effective Malware Detection for Embedded Computing Systems
针对嵌入式计算系统的基于生成式人工智能的有效恶意软件检测
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Sreenitha Kasarapu;Sanket Shukla;Rakibul Hassan;Avesta Sasan;H. Homayoun;Sai Manoj Pudukotai Dinakarrao
  • 通讯作者:
    Sai Manoj Pudukotai Dinakarrao

Sai Manoj Pudukotai Dinakarrao的其他文献

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{{ truncateString('Sai Manoj Pudukotai Dinakarrao', 18)}}的其他基金

Collaborative Research: EAGER: IC-Cloak: Integrated Circuit Cloaking against Reverse Engineering
合作研究:EAGER:IC-Cloak:针对逆向工程的集成电路隐形
  • 批准号:
    2213404
  • 财政年份:
    2022
  • 资助金额:
    $ 30.93万
  • 项目类别:
    Standard Grant
RAPID/Collaborative Research: Developing Pandemics and Healing Models for Coronavirus COVID-19 to Assist in Policy Making
快速/合作研究:开发冠状病毒 COVID-19 的流行病和治疗模型以协助政策制定
  • 批准号:
    2029291
  • 财政年份:
    2020
  • 资助金额:
    $ 30.93万
  • 项目类别:
    Standard Grant

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