FPGA Routing

FPGA Routing

Dr. Philip BriskDepartment of Computer Science and Engineering

University of California, Riverside

CS 223

2

Routing Resource Graph (RRG)

www.eecg.toronto.edu/~aling/ece1718/project/fang/route_rr_graph.png

2-LUT

in1 in2

out

wire1

wire2

wire3 wire4source

sink

out

wire4

wire2

in2in1

wire1

wire3

http://www.eecg.toronto.edu/~aling/ece1718/project/fang/route_rr_graph.png

http://www.eecg.toronto.edu/~aling/ece1718/project/fang/route_rr_graph.png

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FPGA Routing• Disjoint-path problem (on the RRG); NP-complete

• Input:– Graph G(V, E)– Set of sources S = {s1, s2, …, sm}

– Set of sets of sinks T = {T1, T2, …, Tn}, Ti = {ti1, ti

2, …, tik}

• Solution

– Finds paths from each source si to all sinks in Ti

– Paths emanating from different sinks must be disjoint (cannot shared any vertices or edges)

• Objective(s)– Minimize delay, wirelength, etc.

4

Disjoint and Non-disjoint Paths

s1

s2

t21

t11

5


This route is illegal!

s1

s2

t21

t11

Two nets are routed on the same FPGA segment(remember, RRG vertices represent wires and CLB I/Os)

6


This route is legal!

s1

s2

t21

t11

7

LUT Input Equivalence (1/3)

s1

s2

2-LUTin1

in2

s1

s2

2-LUTin1

in2

LUT inputs are interchangeable

8


s1

s2

2-LUTin1

in2

s1

s2

2-LUTin1

in2

s1

s2 t21 = in2

t11 = in1 t2

1 = in1

t11 = in2

s1

s2

Overly restrictive disjoint-path problem formulation

9


s1

s2

2-LUTin1

in2

s1

s2

2-LUTin1

in2

Represent all inputs of a LUT as one RRG sink, t

ts1

s2

PathFinder: A Negotiation-Based Performance-Driven Router for FPGAs

Larry McMurchie and Carl EbelingInternational Symposium on FPGAs, 1995

Architecture and CAD for Deep Submicron FPGAs

(Section 2.2.3, pp. 25-30)

Vaughn Betz, Jonathan Rose, Sandy MarquardtSpringer, 1999

Basic Plan

• Route nets one-by-one– Each net is routed by a priority-driven breadth-first

search– Multiple nets may use the same nodes and edges– Node/edge costs depend on current usage and past

usage history• Repeat for a fixed number of iterations (say 200)– Success: A disjoint routing solution for for all nets– Failure: No disjoint solution found after a fixed number

of iterations

Routability Cost Function (per Node)

• C(n) = p(n) × (b(n) + h(n))

– b(n): base cost of routing through node n• Typically the intrinsic delay of the circuit element corresponding to

the node

– h(n): history cost of routing through node n, based on previous router iterations

– p(n): depends on the number of signals presently routed through node n in the current iteration

Timing-Routability Cost Function

First-Order Congestion

s1

s3

s2

At1

t3

t2

B

C

1

1

1

2

3

4

3

1

1

1

2

3

4

3

First-Order Congestion

s1

s3

s2

At1

t3

t2

B

C

1

1

1

2

3

4

3

1

1

1

2

3

4

3

All paths route through B

• Increase the penalty cost of B per iteration due to sharing

• In a future iteration, it will be cheaper to route signal 1 through A, rather than B

• In a future iteration, it will be cheaper to route signal 2 through C, rather than B

• Requires gradual increase in cost of sharing nodes

Second-order Congestion

s1

s3

s2

At1

t3

t2

B

C

1

2

2

1

1

2

1

2

1

1

Routing Order: 1, 2, 3


Routing Order: 1, 2, 3A

B

C

1

2

2

1

1

2

1

2

1

1

s1

s3

s2

t1

t3

t2



B

C

1

2

2

1

1

2

1

2

1

1

Two paths route through C

• Increasing the history cost through C will push signal 2 onto an alternative path through B

s1

s3

s2

t1

t3

t2



B

C

1

2

2

3

3

2

1

2

3

3


• Increasing the history cost through C will push signal 2 onto an alternative path through B

s1

s3

s2

t1

t3

t2



B

C

1

2

2

3

3

2

1

2

3

3

Two paths route through B

• Increasing the history cost through B will push signal 1 onto an alternative path through A

• Increasing the history cost through B will push signal 2 back onto the path through C

s1

s3

s2

t1

t3

t2



B

C

3

4

2

3

3

4

3

2

3

3

Two paths route through B

• Increasing the history cost through B will push signal 1 onto an alternative path through A

• Increasing the history cost through B will push signal 2 back onto the path through C

s1

s3

s2

t1

t3

t2



B

C

3

4

2

3

3

4

3

2

3

3


• Increasing the history cost through C will push signal 2 back through B

s1

s3

s2

t1

t3

t2



B

C

3

4

2

5

5

4

3

2

5

5


• Increasing the history cost through C will push signal 2 back through B

s1

s3

s2

t1

t3

t2

Pseudocode

Priority driven BFS• Computed from the source

to every sink of net(i)

Connect the newly found path to the current sink to the partly-computed routing tree RT(i) for net I

Timing-Routability Cost Function

Represents the estimated criticality of the path from source I to sink j on net(i)• net(i) may fan out to multiple sinks with varying criticality, depending on placement

Delay of critical path

Negotiated A* Routing for FPGAs

Russell TessierFifth Canadian Workshop on Field Programmable

Devices, 1998

Basic Idea

A* Cost Function

• Cost at a node ni

fi = gi + di

gi = fi-1 + ci

Cost of routing from the source to ni

Estimated cost of routing from ni to the sink

Total cost of previous path

Cost of the next candidate node

A* vs. BFS

A*: fi = gi + di = (fi-1 + ci) + di

BFS: fi = gi

Hybrid: fi = (1 - α)gi + αdi

= (1 - α)(fi-1 + ci) + αdi

Good Ideas, But…

• Algorithm and implementation assumed disjoint switch block– i.e., disjoint global routing domains

• Key issue:– Which routing domains (reachable from the

source) are explored in what order?

A Fast Routability-driven Router for FPGAs

Jordan S. Swartz, Vaughn Betz, Jonathan RoseInternational Symposium on FPGAs, 1998

Architecture and CAD for Deep Submicron FPGAs

(Section 4.3, pp. 76-80)


Speed Enhancements

• Directed depth-first search– Similar to A*, but arguably more aggressive– Key Issues:

• Cost function• Sink (target) ordering for multi-terminal nets

• Route classification– Low-stress– Difficult– Impossible

Cost Function

Cost(n) = C(n) + Costprev + α(ΔD)– Costprev: cost of track segments used to reach the

current segment– C(n): (base) cost of using the track segment

• Increases more rapidly than the original PathFinder algorithm– (ΔD): estimated cost of routing from the current track

segment to the destination • Based on Manhattan (XY) distance)

– α: Direction factor• BFS: α = 0• Directed search: α > 0

Updated Node (Base) Cost Function

• C(n) = p(n) × (b(n) + h(n))– b(n): base cost – h(n): history cost– p(n): usage cost

• C(n) = p(n) × b(n) × h(n) + Bendcost(n, m)– Multiplying b(n) and h(n) eliminates normalization– Penalize global routes that take a lot of turns• These routes are less likely to use long wires

Penalty Cost Function

• Not specified in the original PathFinder paper

p(n) = 1 + max(0, [occupancy(n) – capacity(n)] × pfac)• occupancy(n): number of nets using resource n• capacity(n): maximum number of nets that can use

resource n (typically 1)• pfac: 0.5 for the first iteration; increase by 1.5x to 2x each

subsequent iteration

History Cost Function

• Not specified in the original PathFinder paper

• occupancy(n): number of nets using resource n• capacity(n): maximum number of nets that can use

resource n (typically 1)• hfac: constant; any value between 0.2 and 1 works well

Target Selection

Closest Sink First• Uses fewer track segments

Furthest Sink First• Uses more track segments

Net Order

• Route nets in order of decreasing fan-out

• High fanout nets tend to span the whole FPGA– Easier to route when there is less congestion do to

other nets routed earlier

• Low fanout nets tend to be more localized– Relatively easy to route, even in the presence of

some congestion

Binning

• Only the portions of the net close to the target sink should be expanded– E.g., only expand within

Bin 4 in this example

• Shown to be effective for sinks with more than 50 targets

Bounding Box

• Define a bounding box around source and sinks• Restrict the route for each net to no more than 3

channels outside of the bounding box

Routing High-fanout Nets

Difficulty Prediction

• Since you can’t know Wmin first without routing, use an estimate based on a wirelength prediction model

Routability Results

Routing Times as W increases

Routing Time vs. W (clma Benchmark)

BFS vs. Directed Search (w/Binning)

Difficulty Prediction

• Westimate took less than 1 second to compute

• Some prediction mistakes will happen

• Prediction accuracy was 84%

Fuzzy Prediction

Architecture and CAD for Deep Submicron FPGAs(Section 2.2.4, pp. 31-34,

Section 4.4, pp. 80-89, Section 4.6.4, pp. 99-102 )


VPR Timing-Driven Router

Example Timing Graph

Example Timing Graph

12ns

Timing and Slack

Process vertices in forward topological order

Process vertices in reverse topological order(Required time for any output node is Dmax)

How much can I slow down this circuit element (node) without increasing the critical path?

Elmore Delay of a Source-Sink Path

• Td,i: intrinsic delay of element i (buffer); 0 otherwise

• Ri: equivalent resistance of element i (Rwire, Rbuf, Rpass)

• C(subtreei): total downstream capacitance not isolated by buffers

Equivalent Circuits

Example: M Wire Segments connected by Buffers

Linear and Elmore Delay Models agree!

Example: Chain of Pass Transistors

Linear

Quadratic

Fanout

Introduce Elmore Delay into VPR-PathFinder’s Node Cost Function

Parameters that control how slack affects congestion-delay tradeoff

… And to Support Directed Search• Expected cost to route from a wire segment n to the target

sink j

– The connection will be completed using wires of the same type as node n • Length, connecting switch type, etc.

– There is no congestion along the shortest path to the target • p(m) and h(m) are both 1 for all nodes m on the path that completes the

connection.

Why Optimize Elmore Delay?

Timing-Driven Router

Timing-Driven Routing of High-Fanout Nets

Xun Chen, Jianwen Zhu, Minxuan ZhangInternational Conference on Field Programmable

Logic and Applications, 2011

Motivation (for Binning, Earlier)

Angle-based Pruning

Route the trunk first, then each leaf

NetDecomposition

Results

Routing Algorithms for FPGAs with Sparse Intra-Cluster Routing Crossbars

Yehdhih Moctar, Guy Lemieux, Philip BriskInternational Conference on Field Programmable

Logic and Applications, 2012

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Full Crossbar Intra-cluster Routing2-LUT BLE

in1

in2

2-LUT BLEin1

in2

CLB inputs

Local feedbacks

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RRG

Full Crossbar Intra-cluster Routing

RRG

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Full Crossbar RRG

Don’t model the RRG inside each CLB!

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Sparse Crossbar Intra-cluster Routing (1/6)

2-LUT BLEin1

in2

2-LUT BLEin1

in2

CLB inputs

Local feedbacks

• RRG must include the intra-cluster routing

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Sparse Crossbar Intra-cluster Routing

RRG

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Sparse Crossbar RRG

RRG encompasses the entire FPGA

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Why is this Hard?

• Key Issues relating to RRG size– May run out of memory • If you are not routing in the cloud

– Long router runtimes• Large search space leads to slow convergence• Thrashing

• Objective– Route without expanding the whole RRG

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Routing Algorithms• Extensions to PathFinder routing algorithm

– Routes one net at a time via wavefront expansion

• Baseline– Extend the RRG with intra-cluster routing at each CLB

• Selective RRG Expansion (SERRGE)– Route nets to CLB inputs– Dynamically expand the RRG to finish the route

• Partial Pre-Routing (PPR)– Pre-route all nets within CLBs– Complete global routes to CLB inputs

79

Baseline Router2-LUT BLE

in1

in2

2-LUT BLEin1

in2

CLB inputs

Local feedbacks

• Inputs of the same LUT are equivalent!

Global RRG

80

SERRGE in Action (1/8)

Start with a global RRG

s

t

81


s

t

Route from s’s CLB to t’s CLB

82


s

t

Locally expand the RRG with part of t’s CLB

83

2-LUT BLEin1

in2

2-LUT BLEin1

in2

CLB inputs

Local feedbacks

Maintain one copy of the intra-cluster topology


t

84

2-LUT BLEin1

in2

2-LUT BLEin1

in2

CLB inputs

Local feedbacks

Expand the fanout of the CLB input being routed


t

85

2-LUT BLEin1

in2

2-LUT BLEin1

in2

CLB inputs

Local feedbacks

Complete the route if possible; if not, try again


t

86

2-LUT BLEin1

in2

2-LUT BLEin1

in2

CLB inputs

Local feedbacks

Track used CLB and BLE inputs; remember the route


t

87

2-LUT BLEin1

in2

2-LUT BLEin1

in2

CLB inputs

Local feedbacks

Deallocate unused RRG resources


t

88

SERRGE Summary• Global routing uses standard PathFinder

• Selectively expand portions of the RRG to complete routes from CLB inputs to BLE inputs

• Storage Requirement– Global RRG (standard PathFinder requirement)– One copy of the intra-cluster routing topology– All intra-cluster routes computed thus far

• Fairly challenging to implement!

89

2-LUT BLEin1

in2

2-LUT BLEin1

in2

CLB inputs

Local feedbacks

Process CLBs one at a time

PPR in Action (1/4)

t1

t2

90

2-LUT BLEin1

in2

2-LUT BLEin1

in2

CLB inputs

Local feedbacks

Compute local routes for each BLE

PPR in Action (2/4)

t1

t2

91

2-LUT BLEin1

in2

2-LUT BLEin1

in2

CLB inputs

Local feedbacks

CLB inputs now form equivalence classes.

PPR in Action (3/4)

t1

t2

92

PPR in Action (4/4)

Perform global routing w/equivalence class constraints on CLB inputs

s

t

93

PPR Summary

• Pre-route each CLB

• Propagate LUT equivalences to CLB inputs– A baseline router on the full-blown RRG would still need to

satisfy BLE input-pin equivalence constraints

• Route as normal

• Much easier implementation than SERRGE

Critical Path Delayac

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PPR SERRGE Baseline

RRG Size (MB)ac

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PPR SERRGE Baseline

Router Runtime (s)ac

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FPGA Routing

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