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CAREER: Compiler-Directed Synthesis of Application Specific Processors

CAREER: Compiler-Directed Synthesis of Application Specific Processors
职业:专用处理器的编译器导向综合
批准号:
0347411
负责人:
Scott Mahlke
金额:
$41.0万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2004
资助国家:
美国
项目状态:
已结题
起止时间:
2004-01-15 至 2009-12-31

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英文摘要
CAREER: Compiler-directed Synthesis of Application Specific ProcessorsApplication-specific instruction processors (ASIPs) have great potential to meet the challenging performance, cost, and power demands of the next generation pervasive computing platforms. Three critical issues with ASIPs are design time, design cost, and post-programmability. Automatic design is the key to simultaneously addressing these issues and enabling ASIPs to proliferate. The central vision of this research is to perform architecture synthesis based on compiler technology. Compilers employ sophisticated analyses that are adept at understanding, modeling, and transforming applications. These technologies will be reshaped from producing software customized for a predefined processor to producing hardware specialized to a software application. In this research, an automatic compiler-based synthesis system for ASIPs is being developed, including advances in architecture description technologies, instruction set, datapath, and memory synthesis techniques, and compiler scheduling and code generation algorithms.The broad significance of this work is potentially reshaping the world of pervasive computers. Currently, a select few companies have the necessary resources, expertise, and ability to accomplish a completely designed and verified hardware solution. By creating the enabling technology to go from a software prototype to a specialized-programmable hardware solution, individuals without the necessary hardware expertise will gain the ability to innovate in the pervasive computing domain
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XPS: FULL: CCA: Scalable Approximate Computing for Data Parallel Applications
SHF: Small: Scaling the Compute Efficiency of General-Purpose Processors
CSR: Medium: Collaborative Research: Scaling the Implicitly Parallel Programming Model with Lifelong Thread Extraction and Dynamic Adaptation
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