PLAyer: A Tool for Fast Mapping of Combinational Logic for Design

Report
ABC: An Academic
“Industrial-Strength”
Verification Tool
(based on a tutorial given at CAV’10)
BVSRC
Berkeley Verification and Synthesis
Research Center
UC Berkeley
Robert Brayton, Niklas Een, Alan Mishchenko
Jiang Long, Sayak Ray, Baruch Sterin
Thanks to: NSF, NSA, SRC, and industrial sponsors,
Actel, Altera, Atrenta, IBM, Intel, Jasper, Magma, Oasys,
Real Intent, Synopsys, Tabula, and Verific
Overview





General introduction to ABC
Synergy between synthesis and verification
Introduction to AIGs
Contrast between classical synthesis and ABC synthesis
ABC+ - orchestrated verification flow

Simplification
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Extraction of constraints
Phase abstraction
Forward and minimum FF retimiing
K-step induction
Abstraction
Abstraction
Speculation
Last gasp: BMC, BDDs, interpolation
Verification example
Future work
2
ABC




A synthesis and verification tool under
development at Berkeley
Started 6 years ago as a replacement for SIS
Academic public domain tool
“Industrial-strength”



Has been employed in commercial offerings of
various CAD companies
In both synthesis and verification
Exploits the synergy between synthesis and
verification
3
A Plethora of ABCs
http://en.wikipedia.org/wiki/Abc

ABC (American Broadcasting Company)

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ABC (Active Body Control)

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In C++, these are generic classes at the base of the
inheritance tree; objects of such abstract classes
cannot be created…
Atanasoff-Berry Computer

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ABC is designed to minimize body roll in corner,
accelerating, and braking. The system uses 13
sensors which monitor body movement to supply the
computer with information every 10 ms…
ABC (Abstract Base Class)


A television network…
The Atanasoff–Berry Computer (ABC) was the first
electronic digital computing device.[1] Conceived in
1937, the machine was not programmable, being
designed only to solve systems of linear equations. It
was successfully tested in 1942.
ABC (supposed to mean “as simple as ABC”)

A system for sequential synthesis and verification at
Berkeley
4
Design Flow
ABC - Property
Checking
System Specification
Equivalence checking
RTL
Logic synthesis
Technology mapping
Verification
ABC
Physical synthesis
Manufacturing
5
Areas Addressed by ABC

Combinational synthesis

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AIG rewriting
technology mapping
resynthesis after mapping

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Sequential synthesis

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retiming
structural register sweep
merging seq. equiv. nodes
Verification

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combinational equivalence checking
bounded sequential verification
unbounded sequential verification
equivalence checking using synthesis history
property checking (safety and liveness)
6
Synergy – Two Kinds
1. The algorithms and advancements in
verification can be used in synthesis, and vice
versa.
2. One enables the other

Verification enables synthesis - equivalence
checking capability enables acceptance of
sequential transformations




retiming
use of unreachable states
sequential signal correspondence, etc
Synthesis enables verification

Desire to use sequential synthesis operations (shown by
superior results) spurs verification developments
7
Examples of The Synergy

Similar solutions

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Algorithm migration
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e.g. BDDs, SAT, induction, interpolation, rewriting
Related complexity

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e.g. retiming in synthesis / retiming in verification
scalable synthesis <=> scalable verification
(approximately)
Common data-structures

e.g. combinational and sequential AIGs
8
Evidence of Synergy Between
Synthesis and Verification

IBM

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

ABC


Has a very capable sequential verification engine – SixthSense.
Used throughout IBM to verify property and equivalence
Designers more willing to consider sequential transformations
now.
Sequential verification was developed to check that new
algorithms were implemented correctly
Example of a startup company



Had developed sequential methods to reduce power
Needed a verification tool to double check if their ideas and
implementations were correct.
Needed a tool to assure customers that results were correct.
9
AIG (And-Inverter Graphs)
Definition and Examples
AIG is a Boolean network composed of two-input ANDs and inverters
ab
00
01
11
10
00
0
0
1
0
01
0
0
1
1
11
0
1
1
0
10
0
0
1
0
cd
F(a,b,c,d) = ab + d(ac’+bc)
a
6 nodes
d
b
4 levels
a
a
c
b
c
F(a,b,c,d) = ac’(b’d’)’ + c(a’d’)’ =
ac’(b+d) + bc(a+d)
00
01
11
10
00
0
0
1
0
01
0
0
1
1
11
0
1
1
0
7 nodes
10
0
0
1
0
3 levels
cd
b
a
c
b
d
b
c a
d
10
AIGs

Structural hashing


F = abc
G = (abc)’
H = abc’
Performs AIG compaction
Applied on-the-fly during construction


Propagates constants
Makes each node structurally unique
Before
After
11
AIGs (And-Inverter Graphs)
Why AIGs?
Same reasons hold for both synthesis and verification

Easy to construct, relatively compact, robust

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Can be efficiently stored on disk

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3-4 bytes / AIG node (1M AIG ~ 4Mb file)
Unifying representation

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1M AIG ~ 12Mb RAM
Used by all the different verification engines
Easy to pass around, duplicate, save
Compatible with SAT solvers

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Efficient AIG-to-CNF conversion available
Circuit-based SAT solvers work directly on AIG
“AIGs + simulation + SAT” works well in many cases
12
AIG Memory Usage

Memory allocation

Use fixed amount of memory for each node

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Can be done by a simple custom memory manager
Dynamic fanout manipulation is supported!
Allocate memory for nodes in a topological order

Optimized for traversal in the same topological order

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Mostly AIG can be stored in cache – fewer cache misses.
Small static memory footprint in many applications
Compute fanout information on demand
13
Quick Overview of “Classical”
(technology independent)
Logic Synthesis

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Boolean network
Network manipulation
(algebraic)

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
f
Elimination (substituting a
node into its fanouts)
Decomposition (commondivisor extraction)

xd y ydydxy
yd
z
x+c
xx
Espresso
Don’t cares
Resubstitution (algebraic or
Boolean)
ze
z
Node minimization (Boolean)


Boolean network in SIS
y
abab
cd  cd
e
a
b
c
d
14
“Classical” Logic Synthesis
Boolean network in SIS
Equivalent AIG in ABC
f
f
z
ze
xd  yd  xy
x
z
y
ab
x
cd  cd
y
e
a
b
c
d
e
a b c
d
AIG is a Boolean network of 2-input
15
AND nodes and invertors (dotted lines)
One AIG Node – Many Cuts
Combinational AIG
f
AIG can be used to compute
many cuts for each node

Each cut in AIG represents a
different SIS node


a
b
c
d
e
Different cuts for the same node
SIS node logic represented by AIG
between cut and root.
No a priori fixed boundaries
Implies that AIG manipulation with
cuts is equivalent to working on
many Boolean networks at the
same time
16
Combinational Synthesis
AIG rewriting minimizes the number of AIG nodes without
increasing the number of AIG levels

Rewriting AIG subgraphs

Pre-computing AIG subgraphs

Rewriting node A
Consider function f = abc
Subgraph 1
Subgraph 2
A
A
a b
Subgraph 3

a
c
b
a c
Subgraph 2
Subgraph 1
Rewriting node B
a
a b
a c
b
b
c
B
a
B

c
a
a b
a c
b
c
Subgraph 2
a b
a c
Subgraph 1
In both cases 1 node is saved
17
Combinational Rewriting
iterate 10 times {
for each AIG node {
for each k-cut
derive node output as function of cut variables
if ( smaller AIG is in the pre-computed library )
rewrite using improved AIG structure
}
}
Note: For 4-cuts, each AIG node has, on average, 5 cuts
compared to a SIS node with only 1 cut
Rewriting at a node can be very fast – using hash-table
lookups, truth table manipulation, disjoint decomposition
18
Comparison of Two Syntheses
“Contemporary” synthesis
“Classical” synthesis
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Boolean network
Network manipulation
(algebraic)
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AIG network
DAG-aware AIG rewriting (Boolean)
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Espresso
Don’t cares computed using
BDDs
Resubstitution
Several related algorithms

Elimination
Decomposition (common
kernel extraction)
Node minimization
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Node minimization
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Rewriting
Refactoring
Balancing
Boolean decomposition
Don’t cares computed using
simulation and SAT
Resubstitution with don’t cares
Note: here all algorithms are scalable:
no SOP, no BDDs, no Espresso
19
Sequential Verification

Property checking


Create miter from the design and
the safety property
Special construction for liveness



p
0
Biere, Artho, Schuppan
D1
Equivalence checking


Property checking miter
Create miter from two versions
of the same design
Assuming the initial state is given
The goal is to prove that the
output of the miter is 0, for all
states reachable from the initial
state.
Equivalence checking miter
0
D1
D2
20
Integrated Verification Flow
1.
2.
3.
4.
Simplification
Abstraction
Speculation
High effort verification
21
Integrated Verification Flow
1. Simplification
Initial fast simplification of the logic
 Forward retime and do FF correspondence
 Min FF retime
 Extract implicit constraints** and use them to find signal
equivalences (ABC command scorr –c)

Fold back the constraints
 add a FF so that if ever a constraint is not satisfied, make the
output 0 forever after that.



Trim away irrelevant inputs (do not fanout to FF or POs)
Try phase abstraction (look for periodic signals)
Heavy simplify

(k-step signal correspondence and deep rewriting)
** (see paper of Cabodi et. al.)
22
Sequential SAT Sweeping
(signal correspondence)
Related to combinational CEC
•
Naïve approach
•
Build output miter – call SAT

D1
D2
?
SAT-2
D
?
C
SAT-1
A
B
works well for many easy problems
Better approach - SAT sweeping
• based on incremental SAT solving
• detects possibly equivalent nodes
using simulation
• candidate constant nodes
• candidate equivalent nodes
• runs SAT on the intermediate
miters in a topological order
•
Proving internal equivalences
in a topological order
refines candidates using
counterexamples
23
Improved CEC
For hard CEC instances
 Heuristic: skip some equivalences
 results in

5x reduction in runtime
 solving previously unresolved problems

Given a combinational miter with
equivalence class {A, B, A’, B’}
Possible equivalences:
A = B, A = A’, A = B’, B = A’, B = B’, A’ = B’
A
B
D1
A’
B’
D2
- only try to prove A=A’ and B=B’
- do not try to prove
A = B, A’ = B’, A’ = B
A = B’
24
Sequential SAT Sweeping
(signal correspondence)
Similar to combinational SAT sweeping


detects node equivalences
But the equivalences are sequential


guaranteed to hold only on the reachable state space
Every combinational equivalence is a sequential one
 run combinational SAT sweeping first
A set of sequential equivalences are proved by k-step
induction



Base case
Inductive case
Efficient implementation of induction is key!
25
k-step Induction
Base Case
(just BMC)
Inductive Case
?
Candidate equivalences: {A = B}, {C = D}
Proving internal
equivalences in
a topological
order in frame
k+1
?
SAT-4
D
?
?
C
D
SAT-1
A
B
PIk
k=2
D
PI0
?
C
SAT-1
A
B
initial
state
Proving internal equivalences in
initialized frames 1 through k
Assuming internal
equivalences in
uninitialized frames
1 through k
If proof of any one
equivalence fails
need to start over
0
C
SAT-2
D
?
C
0
SAT-3
A
B
PI1
SAT-2
A
0
B
PI1
D
0
C
A
PI0
B
arbitrary state
26
Efficient Implementation
Two observations:
1.
Both base and inductive cases of k-step induction are
combinational SAT sweeping problems

Tricks and know-how from the above are applicable

base case is just BMC
The same integrated package can be used
 starts with simulation
 performs node checking in a topological order
 benefits from the counter-example simulation
Speculative reduction

Deals with how assumptions are used in the
inductive case

2.
27
Integrated Verification Flow
(continued)
2. Abstraction
 Use new CBA/PBA method*
 Uses single instance of SAT solver
 Uses counter-example based abstraction which
is refined with proof-based abstraction
 Checked afterward with BMC, BDDs, and
simulation for CEX’s and refined with CBA if
found.
* N. Een, A. Mishchenko, and N. Amla, "A single-instance incremental SAT
formulation of proof- and counterexample-based abstraction". Proc. IWLS'10. 28
Integrated Verification Flow
(continued)
3. Speculation **
 Especially useful for SEC
 Simulation used to find candidate equivalences.
 These are equivalences that we could not prove by
induction (sequential SAT sweeping)
 These are used to build a “speculative miter”
 The result is double-checked with BMC, BDDs
or simulation for CEX’s and refined if necessary.
** H. Mony, J. Baumgartner, V. Paruthi, and R. Kanzelman,
“Exploiting suspected redundancy without proving it”.
Proc. DAC’05.
29
Integrated Verification Flow
(continued)
4. Final high-effort verification (prove or disprove)
Prove: (give long run-time limits and/or large
conflict limits)

Try BDD reachability



if problem is small enough (< 200 PI, < 200 FFs)
Try interpolation
Try induction

In rare cases, can prove outputs using induction.
Disprove: (give large run-time and conflict limits)

Try heavy BMC on initial simplified circuit (before
abstraction or speculation done).
30
Interpolation
Input: Sequential AIG with single output representing a property

Property holds when the output is 0

Method: Over-approximate reachability analysis

Using over-approximations, instead of exact sets of reachable states


Output: Proof that
1.
2.
3.

the property holds, or
a real CEX is provided, or
“undecided”
Implementation: A sequence of SAT calls on unrolled time-frames
that is similar to BMC
A
inter
I 21
k
T1
B
T2
T3
L
Tn
~property
P=1
UNSAT
interpolan
t
31
BDD-Based Reachability




Still an important back-end of the verification flow
Also useful to find CEX’s during abstraction and
speculation refinement
Several ideas, old and new, can be put together to
implement a new improved engine
Long live BDDs!
BDD
We tried to ban
BDDs in ABC
32
Examples of Running ABC+
1. Example 1 of simplifying and final proof
with interpolation
2. Prove python code
33
Read_file IE1.aig
PIs = 532, POs = 1, FF = 2389, ANDs = 12049
prove
Example
(use of prove)
Simplifying
Number of constraints = 3
Forward retiming, quick_simp, scorr_constr, trm: PIs = 532, POs = 1, FF = 2342, ANDs = 11054
Simplify: PIs = 532, POs = 1, FF = 2335, ANDs = 10607
Phase abstraction: PIs = 283, POs = 2, FF = 1460, ANDs = 8911
quick_verify
Abstracting
Initial abstraction:
PIs = 1624, POs = 2, FF = 119, ANDs = 1716, max depth = 39
Testing with BMC
bmc3 -C 100000 -T 50 -F 78:
No CEX found in 51 frames
Latches reduced from 1460 to 119
Simplify:
PIs = 1624, POs = 2, FF = 119, ANDs = 1687, max depth = 51
Trimming:
PIs = 158, POs = 2, FF = 119, ANDs = 734, max depth = 51
Simplify:
PIs = 158, POs = 2, FF = 119, ANDs = 731, max depth = 51
quick_verify
Speculating
Initial speculation: PIs = 158, POs = 26, FF = 119, ANDs = 578, max depth = 51
Fast interpolation: reduced POs to 24
Testing with BMC
bmc3 -C 150000 -T 75: No CEX found in 1999 frames
PIs = 158, POs = 24, FF = 119, ANDs = 578, max depth = 1999
Simplify:
PIs = 158, POs = 24, FF = 119, ANDs = 535, max depth = 1999
Trimming:
PIs = 86, POs = 24, FF = 119, ANDs = 513, max depth = 1999
Verifying
Running reach -v -B 1000000 -F 10000 -T 75: BDD reachability aborted
RUNNING interpolation with 20000 conflicts, 50 sec, max 100 frames: 'UNSAT‘
Elapsed time: 457.87 seconds, total: 458.52 seconds
34
Python Code for prove
def prove(a):
global x_factor,xfi,f_name
max_bmc = -1
K=0
set_globals()
status = pre_simp()
if status <= Unsat:
return RESULT[status]
ABC('trm')
ABC('write backup 0’)
K = K +1
set_globals()
if ((n_ands() < 30000) and
(a == 1) and (n_latches() < 300)):
status = quick_verify(0)
if ((status == Unsat) or (status == Sat)):
return RESULT[status]'
status = abstract()
ABC('trm')
status = process_status(status)
if ((status <= Unsat) or status == Error):
return RESULT[status]
ABC('write backup 1’)
K = K +1
if status == Undecided_reduction:
status = quick_verify(1)
status = process_status(status)
if status <= Unsat:
if status == Sat:
status = final_verify_recur(K-1)
return RESULT[status]
if n_ands() > 15000:
K=2
else:
status = speculate()
ABC('trm')
status = process_status(status)
if ((status == Unsat) or status == Error):
return RESULT[status]
if status == Sat:
K = K-1
else:
ABC('write backup 2’)
K = K +1
status = final_verify_recur(K)
return RESULT[status]
35
Verification Engines (Summary)

Simplifiers


Combinational synthesis
Sequential synthesis





Re-parametrization (not implemented yet in ABC currently)
Retiming (most forward and minimum FF)
Bug-hunters (also part of abstraction methods)





Retiming
Sequential SAT sweeping (k-step induction)
random simulation (sequential)
bounded model checking (BMC)
hybrid of simulation and BMC (“semi-formal”)
BDD reachability
Provers




K-step induction, with and without constraints
Interpolation (over-approximate reachability)
BDDs (exact reachability)
Explicit state space enumeration (‘era’)
36
Future Work

Improved BDD reachability engine (we hope)

We have three




Improved interpolation engine


One is quite weak (HWMCC’08)
We have just integrated a better one
May have a much better one later.
Working on a new version
Improved circuit-based SAT solver

Currently used in signal correspondence to simplify
larger circuits


Faster but sometimes limited quality
Will be improved to see if it can compete with MiniSat
1.14c
37
To Learn More

Read recent papers
http://www.eecs.berkeley.edu/~alanmi/publications

Send email



[email protected]
[email protected]
Visit BVSRC webpage www.bvsrc.org
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end
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