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A high-level clustering algorithm targeting dual Vdd FPGAs
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ACM Transactions on Design Automation of Electronic Systems (TODAES) archive
Volume 13 ,  Issue 4  (September 2008) table of contents
Article No. 57  
Year of Publication: 2008
ISSN:1084-4309
Authors
Rajarshi Mukherjee  Synopsys, Inc., Mountain View, CA
Song Liu  Northwestern University, Evanston, IL
Seda Ogrenci Memik  Northwestern University, Evanston, IL
Somsubhra Mondal  Neokast Inc., Evanston, IL
Publisher
ACM  New York, NY, USA
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ABSTRACT

Recent advanced power optimizations deployed in commercial FPGAs, laid out a roadmap towards FPGA devices that can be integrated into ultra low power systems. In this article, we present a high-level design tool to support the process of mapping an application onto a FPGA device with dual supply voltages. Our main contribution in this paper is an algorithm, which creates voltage scaling ready clusters by utilizing the timing slack available in the designs. We propose to first create clusters of CLBs within a given CLB-level netlist. This clustering algorithm intends to group chains of CLBs possessing similar amounts of timing slack along their critical path together. Once these clusters are identified, they are placed onto respective Vdd partitions on the device. We have evaluated different dual Vdd fabrics and the potential gain in power consumption is explored. When a subset of the logic blocks on the device can be driven by low Vdd levels (either with a dedicated low Vdd supply or with a programmable selection between low and high Vdd levels for these blocks) this affects placement and routing. As a result the maximum frequency of the designs may be affected. In order to evaluate the overall impact of creating voltage islands, we measured the Energy-Delay Product for our benchmark designs. We observed that the Energy-Delay product can be decreased by 26.9% when the placement of the designs into different voltage levels is guided by our clustering algorithm.


REFERENCES

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Collaborative Colleagues:
Rajarshi Mukherjee: colleagues
Song Liu: colleagues
Seda Ogrenci Memik: colleagues
Somsubhra Mondal: colleagues