[mod.ai] Seminar - AI, Mathematical Programming, and VLSI Design

liew@ARAMIS.RUTGERS.EDU.UUCP (03/26/87)

The next design colloquim will be held on Thursday (march 26th) at
1:30pm in TCB 103.  Most of you are unfamiliar with the location of
TCB 103 so we will meet at Hill 423 at 1:15 and proceed from there.
The speaker will be Wayne Wolf of ATT Bell Laboratories and the title
of his talk is "Artificial Intelligence, Mathematical Programming and
VLSI Design".  The suggested readings are:

Wolf, Kowalski, McFarland "Knowledge Engineering Issues in VLSI
Synthesis", AAAI-86.

Brayton, et al., "Multiple-Level Logic Optimization System",
ICCAD-86, pp. 356-360.

Gregory, et al., "SOCRATES: A System for Automatically Synthesizing
and Optimizing Combinational Logic", DAC-86, pp. 79-85.

Shin and Sangiovanni-Vincentelli, "MIGHTY: A Rip-Up and Reroute
Detailed Router", ICCAD-86, pp. 2-5.

Joobani, "WEAVER: A Knowledge-Based Routing Expert", PhD
dissertation, CMU, 1985.

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Abstract:

Title: Artificial Intelligence, Mathematical Programming, and VLSI Design
Speaker: Wayne Wolf, AT&T Bell Laboratories, Murray Hill

	Artifical intelligence techniques have found their
greatest success in diagnosis and classification problems.  The
application of AI to design problems is relatively new.  In
this talk I want to consider how the intellectual tools that
AI brings to the design problem can best be used by contrasting
two paradigms: artificial intelligence and mathematical programming.
I will argue that mathematical programming is a more powerful
paradigm than AI for a lot of synthesis problems because
mathematical programming a) allows better application of 
brute force; b) encourages us to formulate solvable problems.
I will argue that AI is a more powerful paradigm for
knowledge representation because it provides a lot of tools
for separating particular pieces of knowledge from the
engines used to maintain them.

	The talk will be in three parts:
	1) The VLSI design problem: what is hard about VLSI
design; what tools people need to make bigger, better designs;
what people would do with VLSI synthesis if they had it.

	2) Synthesis and search: search in AI and mathematical
programming; problem formulation and search; results in
application of AI and mathematical programming techniques
to some design problems.

	3) Synthesis and knowledge representation: why
knowledge representation is important; examples of KR
problems and solutions from Fred, the database; how
AI knowledge representation and mathematical programming
complement each other in Lucy, the controller designer.