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Faster Logic Manipulation for Large DesignsPowerPoint Presentation

Faster Logic Manipulation for Large Designs

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Faster Logic Manipulation for Large Designs

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Faster Logic Manipulation for Large Designs

Alan Mishchenko Robert Brayton

University of California, Berkeley

- Motivation
- Simple, local, iterative computation
- Millions of 6-16 input functions (“small practical functions” = SPFs)
- Runtime / memory / quality can be improved

- Choosing the canonical form
- Historic viewpoint
- Disjoint-support decomposition (DSD)
- DSD manager
- Impact on computations

- Experimental results
- Statistics of DSD functions
- Runtime improvements
- Typical DSD structures

- Conclusions

- Primitive gates
- Const0
- AND
- XOR
- PRIME
- 2:1 MUX
- Majority

d

e

c

a

b

f

g

h

i

k

- Canonical form
- R. L. Ashenhurst, “The decomposition of switching functions”. Computation Lab, Harvard University, 1959, Vol. 29, pp. 74-116.

- Computation from cofactors
- V. Bertacco and M. Damiani, "Disjunctive decomposition of logic functions," Proc. ICCAD ‘97, pp. 78-82.

- Computation from cofactors (corrections)
- Y. Matsunaga, "An exact and efficient algorithm for disjunctive decomposition", Proc. SASIMI '98, pp. 44-50.

- Alternative computations
- T. Sasao and M. Matsuura, "DECOMPOS: An integrated system for functional decomposition," Proc. IWLS ’98, pp. 471-477.
- S.-I. Minato and G. De Micheli, “Finding all simple disjunctive decompositions using irredundant sum-of-products forms”. Proc. ICCAD’98, pp. 111-117.

- Boolean operations
- S. Plaza and V. Bertacco, "Boolean operations on decomposed functions", Proc. IWLS ’05.

- Applications in synthesis and mapping
- A. Mishchenko, R. K. Brayton, and S. Chatterjee, "Boolean factoring and decomposition of logic networks", Proc. ICCAD'08, pp. 38-44.

- Truth tables (TTs) (< 1980)
- Sums-of-products (SOPs) (1980-1990)
- Binary decision diagrams (BDDs) (1990-2000)
- And-inverter graphs (AIGs) and truth tables (2000-2012)
- Disjoint-support decompositions (DSDs) (> 2012)
- For small practical functions (SPFs) only

- TTs dominate BDDs in terms of memory and runtime
- TTs and BDDs are equally (in)convenient for detecting Boolean properties
- DSDs take less memory/runtime than BDDs/TTs for pratical functions of K inputs (8 < K < 16)
- DSDs explicitly represent Boolean properties
- Symmetry, unateness, NPN canonicity, decomposability, etc
- Very important for practical applications!

- Similar to BDD manager
- Maintains canonical forms
- Performs Boolean operations
- Employs computed table

- Different from BDD manager
- Different data structure
- Different normalization rules
- More reusable computed table

- One constant 0 node
- One primary input node n
- Multi-input AND and XOR nodes with ordered fanins
- Three-input MUX nodes
- Multi-input PRIME nodes
- Non-decomposable functions

- Propagating inverters
- AND(!a, !XOR(b, c)) AND(n, XOR(n, n))

- Collapsing operators
- AND(a, AND(b, !AND(c, d)) AND(n, n, !AND(n, n))

- Ordering faninsof AND/XOR
- Use support size
- If there is a tie, AND precedes XOR precedes MUX precedes PRIME.
- If there is a tie, a non-inverted fanin precedes a inverted fanin.
- If there is a tie, the fanins’ fanins are ordered and compared in their selected order
- If the recursive comparison fails to produce a unique order, the fanins’ DSD structures are isomorphic and therefore their order is immaterial.

- Unifying variables
- AND(a, XOR(b, c), MUX(d, e, f)) AND(n, XOR(n, n), MUX(n, n, n))

!a = NOT( a )

(ab) = AND( a, b )

[ab] = XOR( a, b )

<abc> = MUX( a , b , c )

- (Re)invented DSD
- Canonical form, which exposes Boolean properties

- Introduced a DSD package
- An alternative to a BDD package for SPFs

- Discussed preliminary experimental results
- Exciting future work is waiting to be done!

When logic transformations, such as circuit restructuring, technology mapping, and post-mapping optimization, are repeatedly applied to large hardware designs, millions of relatively small (6-16 input) Boolean functions have to be efficiently manipulated. This paper focuses on a novel representation of these small functions, in terms of their disjoint-support decomposition (DSD) structures. A new DSD manipulation package is developed, which allows for faster logic manipulation compared to known methods.