Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 62 tok/s
Gemini 2.5 Pro 51 tok/s Pro
GPT-5 Medium 36 tok/s Pro
GPT-5 High 30 tok/s Pro
GPT-4o 67 tok/s Pro
Kimi K2 192 tok/s Pro
GPT OSS 120B 430 tok/s Pro
Claude Sonnet 4.5 34 tok/s Pro
2000 character limit reached

A Partitioning Methodology for Accelerating Applications in Hybrid Reconfigurable Platforms (0710.4844v1)

Published 25 Oct 2007 in cs.AR

Abstract: In this paper, we propose a methodology for partitioning and mapping computational intensive applications in reconfigurable hardware blocks of different granularity. A generic hybrid reconfigurable architecture is considered so as the methodology can be applicable to a large number of heterogeneous reconfigurable platforms. The methodology mainly consists of two stages, the analysis and the mapping of the application onto fine and coarse-grain hardware resources. A prototype framework consisting of analysis, partitioning and mapping tools has been also developed. For the coarse-grain reconfigurable hardware, we use our previous-developed high-performance coarse-grain data-path. In this work, the methodology is validated using two real-world applications, an OFDM transmitter and a JPEG encoder. In the case of the OFDM transmitter, a maximum clock cycles decrease of 82% relative to the ones in an all fine-grain mapping solution is achieved. The corresponding performance improvement for the JPEG is 43%.

Citations (12)

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.