Creat membership Creat membership
Sign in

Forgot password?

Confirm
  • Forgot password?
    Sign Up
  • Confirm
    Sign In
Creat membership Creat membership
Sign in

Forgot password?

Confirm
  • Forgot password?
    Sign Up
  • Confirm
    Sign In
Collection
For ¥0.57 per day, unlimited downloads CREATE MEMBERSHIP Download

toTop

If you have any feedback, Please follow the official account to submit feedback.

Turn on your phone and scan

home > search >

A multi-model power estimation engine for accuracy optimization

Author:
F. Klein   G. Araujo   R. Azevedo   R. Leao   L. C. V. Dos Santos  


Issue Date:
2007


Abstract(summary):

RTL power macromodeling is a mature research topic with a variety of equation and table-based approaches. Despite its maturity, macromodeling is not yet widely accepted as an industrial de facto standard for power estimation at the RT level. Each approach has many variants depending upon the parameters chosen to capture power variation. Every macromodeling technique has some intrinsic limitation affecting either its performance or its accuracy. Therefore, alternative macromodeling methods can be envisaged as part of a power modeling toolkit from which the most suitable method for a given component should be automatically selected. This paper describes a new multi-model power estimation engine that selects the macromodeling technique leading to the least estimation error for a given system component depending on the properties of its input-vector stream. A proper selection function is built after component characterization and used during estimation. Experimental results show that our multi-model engine improves the robustness of power analysis with negligible usage overhead. Accuracy becomes 3 times better on average, as compared to conventional single-model estimators, while the overall maximum estimation error is divided by 8.


VIEW PDF

The preview is over

If you wish to continue, please create your membership or download this.

Create Membership

Similar Literature

Submit Feedback

This function is a member function, members do not limit the number of downloads