EECS 219B Spring 2002 - University of Texas at Austin

EECS 219B Spring 2002 - University of Texas at Austin

Logic Synthesis Multi-Level Logic Synthesis Courtesy RK Brayton (UCB) and A Kuehlmann 1 Basic Model: Hardware Implementing Finite State Machines Y=(y1,y2,,yn) X=(x1,x2,,xn) S=(s1,s2,,sn)

D S=(s1,s2,,sn) M(X,Y,S,S0,,): X: Inputs Y: Outputs S: Current State S0: Initial State(s) : X S S (next state function) : X S Y (output function) Delay element: Clocked: synchronous

single-phase clock, multiple-phase clocks Unclocked: asynchronous 2 General Logic Structure Combinational optimization keep latches/registers at current positions, keep their function optimize combinational logic in between Sequential optimization change latch position/function 3 Optimization Criteria for Synthesis 1. 2.

3. 4. 5. 6. 7. The optimization criteria for multi-level logic is to minimize some function of: Area occupied by the logic gates and interconnect (approximated by literals = transistors in technology independent optimization) Critical path delay of the longest path through the logic Degree of testability of the circuit, measured in terms of the percentage of faults covered by a specified set of test vectors for an approximate fault model (e.g., single or multiple stuck-at faults) Power consumed by the logic gates Noise immunity Placeability, Wireability

Manufacturability while simultaneously satisfying upper or lower bound constraints placed on these physical quantities 4 Example: Area-Delay Trade-off 5 Two-Level (PLA) vs. Multi-Level E.g. Standard Cell Layout PLA Multi-level Logic control logic constrained layout

highly automatic technology independent multi-valued logic input, output, state encoding Very predictable all logic general (e.g. standard cell, regular blocks,..) automatic partially technology independent some ideas part of multi-level logic Very hard to predict 6 General Approaches to Synthesis PLA Synthesis: theory well understood

predictable results in a top-down flow Multi-Level Synthesis: optimization criteria very complex except niches, no general theory available greedy optimization approach incrementally improve along various dimensions of the criteria works on common design representation (circuit or network representation) attempt a change, accept if criteria improves, otherwise reject 7 Transformation-based Synthesis all modern synthesis systems are build that way set of transformations that change network representation work on uniform network representation script of scenario that can combine those transformations to a

overall greedy transformations differ in: the scope they are applied local scope versus global restructuring the domain they optimize combinational versus sequential timing versus area technology independent versus technology dependent the underlying algorithms they use BDD based, SAT based, structure based 8 Network Representation Boolean network: directed acyclic graph (DAG) node logic function representation fj(x,y) node variable yj: yj= fj(x,y)

edge (i,j) if fj depends explicitly on yi Inputs x = (x1, x2,,xn ) Outputs z = (z1, z2,,zp ) External dont cares d1(x), d2(x) ,, dp(x) 9 Typical Synthesis Scenario RTL to Network Transformation - read Verilog - control/data flow analysis Technology independent Optimizations - basic logic restructuring

- crude measures for goals Technology Mapping Technology Dependent Optimizations Test Preparation - use logic gates from target cell library - timing optimization - physically driven optimizations - improve testability - test logic insertion 10 Local versus Global Transformations

Local transformations optimize the function of one node of the network smaller area faster performance map to a particular set of cells Global transformations restructure the entire network merging nodes spitting nodes removing/changing connections between nodes Node representation: SOP, POS BDD

Factored forms keep size bounded to avoid blow-up of local transformations 11 Sum of Products (SOP) Example: abc+abd+bd+bef (sum of cubes) Advantages: easy to manipulate and minimize many algorithms available (e.g. AND, OR, TAUTOLOGY) two-level theory applies Disadvantages: Not representative of logic complexity. For example f=ad+ae+bd+be+cd+ce f=abc+de These differ in their implementation by an inverter. hence not easy to estimate logic; difficult to estimate progress during logic manipulation

12 Reduced Ordered BDDs like factored form, represents both function and complement like network of muxes, but restricted since controlled by primary input variables not really a good estimator for implementation complexity given an ordering, reduced BDD is canonical, hence a good replacement for truth tables for a good ordering, BDDs remain reasonably small for complicated functions (e.g. not multipliers) manipulations are well defined and efficient true support (dependency) is displayed 13

Factored Forms Example: (ad+bc)(c+d(e+ac))+(d+e)fg Advantages good representative of logic complexity f=ad+ae+bd+be+cd+ce f=abc+de f=(a+b+c) (d+e) in many designs (e.g. complex gate CMOS) the implementation of a function corresponds directly to its factored form good estimator of logic implementation complexity doesnt blow up easily Disadvantages not as many algorithms available for manipulation hence often just convert into SOP before manipulation 14

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