
Fang Fang
Carnegie Mellon University

Efficient Static Analysis of FinitePrecision
Effects via Affine Arithmetic Modeling
Modern digital signal processing (DSP) applications
are typically prototyped using
floatingpoint arithmetic, which
offers both large dynamic range
and high precision for each numerical computation. However, for hardware
implementations, the final form rarely uses a fullprecision
floatingpoint unit, given constraints
of silicon area, power, and speed. This creates the commonand still
awkwardproblem of transforming the application from its initial,
for all practical purposes "infinite" precision
form, into some final, finiteprecision
hardware format. A common approach
is to run detailed simulation with a finite set of random inputs
to capture the necessary numerical ranges and the maximum error.
In this talk, I introduce a static error analysis technique, based
on smart interval methods from affine arithmetic, to help designers
translate DSP codes from fullprecision floatingpoint to smaller
finiteprecision formats. The technique gives results for numerical
error estimation comparable to detailed simulation, but achieves
speedups of four orders of magnitude by avoiding actual bitlevel
simulation.
Bio
Fang Fang received the B.S.E.E degree from Southeast University,
China in 1999 and the M.S. Degree from Carnegie Mellon University,
Pittsburgh, PA in 2001. Currently she is a Ph.D. candidate in
Electrical and Computer Engineering Department at Carnegie Mellon
University with Prof. Rob A. Rutenbar and Prof. Tsuhan Chen as
advisors. Her research interest includes numerical performance
of signal processing algorithms, VLSI multimedia processing and
high level hardware synthesis of DSP algorithms. Her current
work focuses on VLSI design of continuous speech recognition
system.
