336x Filetype PPT File size 0.43 MB Source: vlsicad.ucsd.edu
Motivation
• Early prediction of design characteristics
• Interconnect wirelength
• Done
Interconnect fanout
• Clock frequency
• Ongoing
Area, etc.
• Enable early-stage design space exploration
• Abstractions of physically achievable system
implementations
• Models to drive efficient system-level optimizations
• Existing models fail to capture the impact of (1)
architectural and (2) implementation parameters
• Significant deviation against layout data
UCSD VLSI CAD Laboratory - ISOCC, Nov. 23, 2009 (2/12)
Existing Models
• Wirelength statistics
• Christie et al. [2000]
• Point-to-point wirelength distribution based on Rent’s rule
• Extends Davis et al. wirelength distribution model
• Significant deviation against layout data
• Fanout statistics
• Zarkesh-Ha et al. [2000]
• Error in counting the number of m-terminal nets per gate
• Significant deviation against layout data
• Existing models fail to take into account combined impacts of
architectural and implementation parameters
Question: What is the impact of considering architectural
parameters in early prediction of physical implementation?
UCSD VLSI CAD Laboratory - ISOCC, Nov. 23, 2009 (3/12)
Implementation Flow and Tools
Router / DFT RTL Architectural
(Netmaker / SPIRAL) Parameters
Synthesis
(Design Compiler)
Implementation
Parameters Wirelength and
Place + Route Fanout Models
(SOC Encounter)
Model Generation
Wiring Reports (Multiple Adaptive
Regression Splines)
• Timing-driven synthesis, place and route flow
• Consider both architectural and implementation parameters for
more complete modeling of design space
• Rent parameter extraction through internal RentCon scripts
UCSD VLSI CAD Laboratory - ISOCC, Nov. 23, 2009 (4/12)
Design of Experiments
• Netmaker generation of fully synthesizable router RTL code
• SPIRAL generation of fully synthesizable DFT RTL code
• Libraries: TSMC (1) 130G, (2) 90G, and (3) 65GP
• Tools: Netmaker (University of Cambridge), SPIRAL (CMU),
Synopsys Design Compiler and PrimeTime, Cadence SOC
Encounter, Salford MARS 3.0
• Experimental axes:
• Technology nodes: {130nm, 90nm, 65nm}
• Clock frequency
• Aspect ratio
• Row utilization
• Architectural parameters: {fw, nvc, nport, lbuf} for routers and
{n, width, t, nfifo} for DFT cores
UCSD VLSI CAD Laboratory - ISOCC, Nov. 23, 2009 (5/12)
Modeling Problem
→
• Accurately predict y given vector of parameters x
→
• Difficulties: (1) which variables x to use, and (2) how different
variables combine to generate y
→
y = f(x)+noise
• Parametric regression: requires a functional form
• Nonparametric regression: learns about the best model from
the data itself
For our purpose, allows decoupling of underlying
architecture / implementation from modeling effort
• We use nonparametric regression to model interconnect
wirelength (WL) and fanout (FO)
UCSD VLSI CAD Laboratory - ISOCC, Nov. 23, 2009 (6/12)
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