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Low-Energy Datapath Design for Programmable Digital Signal Processors

Low-Energy Datapath Design for Programmable Digital Signal Processors
可编程数字信号处理器的低能耗数据路径设计
批准号:
9988262
负责人:
Keshab Parhi
金额:
$32.05万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2000
资助国家:
美国
项目状态:
已结题
起止时间:
2000-11-01 至 2003-10-31

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中文摘要
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英文摘要
This research concentrates on design of low-energy application-specific datapaths, low-energy schedules, and design tradeoffs with respect to number of datapaths, level of pipelining and scheduling approaches. Applications are to datapath design for low-energy implementation of digital signal processing and cryptography systems using programmable digital signal processors. A VLIW (very long instruction word) type programmable DSP (PDSP) is assumed. In such a PDSP, different datapaths can be independently activated through the VLIW instruction word. This research seeks modifications of datapaths in existing PDSP and design of new datapaths that do not exist in current PDSPs for low power implementations. The emphasis here is on simultaneous datapath and scheduling or software co-selection. It is important not only to design the best datapaths but also to design best scheduling approaches to reduce switching activity in the datapaths to reduce energy consumption. In the context of fixed and adaptive FIR digital filters and equalizers, the goal is to find datapaths which are best suited to transpose-form FIR filters, to use strength-reduced parallel structures to reduce the number of clock cycles and to perform scheduling by switching activity. In RLS adaptive filters for space-time adaptive processing and multi-user detection problems in wireless communications, a hierarchical scheduling approach is being explored. Here a globally parallel locally sequential partitioning approach is adopted where each partition is mapped to a distinct CORDIC datapath. Within each partition Givens' rotation operations are scheduled to reduce switching activity. In elliptic cryptosystem design, the goal is to reduce energy consumption by appropriate hardware-software codesign and parametric tradeoff analysis.
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Collaborative Research: SHF: Small: Efficient and Scalable Privacy-Preserving Neural Network Inference based on Ciphertext-Ciphertext Fully Homomorphic Encryption
  • 批准号:
    2243053
  • 项目类别:
    Standard Grant
  • 资助金额:
    $32.5万
  • 财政年份:
    2023
  • 负责人:
    Keshab Parhi
  • 依托单位:
Collaborative Research: SHF: Medium: TensorNN: An Algorithm and Hardware Co-design Framework for On-device Deep Neural Network Learning using Low-rank Tensors
  • 批准号:
    1954749
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $40.0万
  • 财政年份:
    2020
  • 负责人:
    Keshab Parhi
  • 依托单位:
SHF: Small: Collaborative Research: LDPD-Net: A Framework for Accelerated Architectures for Low-Density Permuted-Diagonal Deep Neural Networks
  • 批准号:
    1814759
  • 项目类别:
    Standard Grant
  • 资助金额:
    $27.5万
  • 财政年份:
    2018
  • 负责人:
    Keshab Parhi
  • 依托单位:
EAGER: Low-Energy Architectures for Machine Learning
  • 批准号:
    1749494
  • 项目类别:
    Standard Grant
  • 资助金额:
    $12.5万
  • 财政年份:
    2017
  • 负责人:
    Keshab Parhi
  • 依托单位:
国内基金
海外基金
度量测度空间上基于狄氏型和p-energy型的热核理论研究
  • 批准号:
    QN25A010015
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2025
  • 负责人:
    高晋
  • 依托单位: