CS184a: Computer Architecture (Structures and Organization)

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Presentation transcript:

CS184a: Computer Architecture (Structures and Organization) Day13: November 6, 2000 Interconnect Richness Caltech CS184a Fall2000 -- DeHon

Last Time Rent’s Rule Implication Superlinear growth rate of interconnect p>0.5 => Area growth O(N2p) Just starting to look at balancing interconnect and logic Caltech CS184a Fall2000 -- DeHon

Today How rich should interconnect be specifics of understanding interconnect methodology for attacking these kinds of questions Caltech CS184a Fall2000 -- DeHon

Now What? There is structure (locality) Rent characterizes locality How rich should interconnect be? Allow full utilization? Most area efficient? Model requirements and area impact Caltech CS184a Fall2000 -- DeHon

Step 1: Build Architecture Model Assume geometric growth Pick parameters: Build architecture can tune F, C a, p Caltech CS184a Fall2000 -- DeHon

Tree of Meshes Tree Restricted internal bandwidth Can match to model Caltech CS184a Fall2000 -- DeHon

Parameterize C Caltech CS184a Fall2000 -- DeHon

Parameterize Growth (2 1)* => a=2 (2 2 2 1)* =>a=2(3/4) Caltech CS184a Fall2000 -- DeHon

Step 2: Area Model Need to know effect of architecture parameters on area (costs) focus on dominant components wires switches logic blocks(?) Caltech CS184a Fall2000 -- DeHon

Area Parameters Alogic = 40Kl2 Asw = 2.5Kl2 Wire Pitch = 8l Caltech CS184a Fall2000 -- DeHon

Switchbox Population Full population is excessive (next lecture) Hypothesis: linear population adequate still to be (dis)proven Caltech CS184a Fall2000 -- DeHon

“Cartoon” VLSI Area Model (Example artificially small for clarity) Caltech CS184a Fall2000 -- DeHon

Larger “Cartoon” 1024 LUT Network P=0.67 LUT Area 3% Caltech CS184a Fall2000 -- DeHon

Effects of P (a) on Area P=0.5 P=0.67 P=0.75 1024 LUT Area Comparison Caltech CS184a Fall2000 -- DeHon

Effects of P on Capacity Caltech CS184a Fall2000 -- DeHon

Step 3: Characterize Application Requirements Identify representative applications. Today: IWLS93 logic benchmarks How much structure there? How much variation among applications? Caltech CS184a Fall2000 -- DeHon

Application Requirements Max: C=7, P=0.68 Avg: C=5, P=0.72 Caltech CS184a Fall2000 -- DeHon

Benchmark Wide Caltech CS184a Fall2000 -- DeHon

Benchmark Parameters Caltech CS184a Fall2000 -- DeHon

Complication Interconnect requirements vary among applications Interconnect richness has large effect on area What is effect of architecture/application mismatch? Interconnect too rich? Interconnect too poor? Caltech CS184a Fall2000 -- DeHon

Interconnect Mismatch in Theory Caltech CS184a Fall2000 -- DeHon

Step 4: Assess Resource Impact Map designs to parameterized architecture Identify architectural resource required Compare: mapping to k-LUTs; LUT count vs. k. Caltech CS184a Fall2000 -- DeHon

Mapping to Fixed Wire Schedule Easy if need less wires than Net If need more wires than net, must depopulate to meet interconnect limitations. Caltech CS184a Fall2000 -- DeHon

Mapping to Fixed-WS Better results if “reassociate” rather than keeping original subtrees. Caltech CS184a Fall2000 -- DeHon

Observation Don’t really want a “bisection” of LUTs subtree filled to capacity by either of LUTs root bandwidth May be profitable to cut at some place other than midpoint not require “balance” condition “Bisection” should account for both LUT and wiring limitations Caltech CS184a Fall2000 -- DeHon

Challenge Not know where to cut design into not knowing when wires will limit subtree capacity Caltech CS184a Fall2000 -- DeHon

Brute Force Solution Explore all cuts start with all LUTs in group consider “all” balances try cut recurse Caltech CS184a Fall2000 -- DeHon

Brute Force Too expensive Exponential work …viable if solving same subproblems Caltech CS184a Fall2000 -- DeHon

Simplification Single linear ordering Partitions = pick split point on ordering Reduce to finding cost of [start,end] ranges (subtrees) within linear ordering Only n2 such subproblems Can solve with dynamic programming Caltech CS184a Fall2000 -- DeHon

Dynamic Programming Start with base set of size 1 Compute all splits of size n, from solutions to all problems of size n-1 or smaller Done when compute where to split 0,N-1 Caltech CS184a Fall2000 -- DeHon

Dynamic Programming Just one possible “heuristic” solution to this problem not optimal dependent on ordering sacrifices ability to reorder on splits to avoid exponential problem size Opportunity to find a better solution here... Caltech CS184a Fall2000 -- DeHon

Ordering LUTs Another problem lay out gates in 1D line minimize sum of squared wire length tend to cluster connected gates together Is solvable mathematically for optimal Eigenvector of connectivity matrix Use this 1D ordering for our linear ordering Caltech CS184a Fall2000 -- DeHon

Mapping Results Caltech CS184a Fall2000 -- DeHon

Step 5: Apply Area Model Assess impact of resource results Caltech CS184a Fall2000 -- DeHon

Resources  Area Model  Area Caltech CS184a Fall2000 -- DeHon

Net Area Caltech CS184a Fall2000 -- DeHon

Picking Network Design Point Don’t optimize for 100% compute util. (100% yield) …also don’t optimize for highest peak. Caltech CS184a Fall2000 -- DeHon

What about a single design? Caltech CS184a Fall2000 -- DeHon

LUT Utilization predict Area? Single design Caltech CS184a Fall2000 -- DeHon

Methodology Architecture model (parameterized) Cost model Important task characteristics Mapping Algorithm Map to determine resources Apply cost model Digest results find optimum (multiple?) understand conflicts (avoidable?) Caltech CS184a Fall2000 -- DeHon

Big Ideas [MSB Ideas] Interconnect area dominates logic area Interconnect requirements vary among designs within a single design To minimize area focus on using dominant resource (interconnect) may underuse non-dominant resources (LUTs) Caltech CS184a Fall2000 -- DeHon

Big Ideas [MSB Ideas] Two different resources here compute, interconnect Balance of resources required varies among designs (even within designs) Cannot expect full utilization of every resource Most area-efficient designs may waste some compute resources (cheaper resource) Caltech CS184a Fall2000 -- DeHon