ECE 3060 VLSI and Advanced Digital Design Lecture 16 Technology - PowerPoint PPT Presentation

ECE 3060 VLSI and Advanced Digital Design Lecture 16 Technology Mapping/Library Binding

Outline • Modeling and problem analysis • Rule-based systems for library binding • Algorithms for library binding – structural covering/matching – boolean covering/matching • Concurrent minimization and binding Disclaimer: lecture notes based on originals by Giovanni De Micheli

Technology Mapping/Library Binding • Given an unbound logic network, already minimized, and a set of library cells – transform into an interconnection of instances of library cells – various design goals 8 optimize area (under delay constraints) 8 optimize delay (under area constraints) 8 optimize power (under delay constraints) • Method used for redesigning circuits in different technologies

Library models • Combinational elements – single-output functions 8 e.g., AND, OR, AOI – compound cells 8 e.g., adders, encoders • Sequential elements – registers, counters • Miscellaneous – three-state drivers

Major Approaches • Rule-based systems: – mimic designer activity – handle all types of cells • Heuristic algorithms – In general, restricted to single-output combinational cells • Most tools use a combination of both rule- based and heuristic algorithms – e.g., replace adders in design with adders in library (rule-based), use heuristic algorithms for random logic

Rule-based library binding • Binding by stepwise transformations • Data-base – set if patterns associated with best implementation • Rules – select subnetwork to be mapped – handle high-fanout problems, buffering, etc.

Example

Algorithms for library binding • Mainly for single-output combinational cells • Fast and efficient – quality comparable to rule-based systems • Library description/update is simple – each cell modeled by its function or equivalent pattern

Problem analysis • To do library binding (technology mapping), we need to solve two subproblems: – Matching 8 a cell matches a subnetwork if their terminal behaviors are the same 8 input-variable assignment problem – Covering 8 a cover of an unbound network is a partition into subnetworks which can be replaced by library cells

Assumptions • Network granularity is fine – decomposition into base functions 8 2-input AND, OR, NAND, NOR • Trivial binding – replacement of each vertex by base cell

Example

Example

Example • Vertex covering: – cover v 1 : m 1 + m 4 + m 5 – cover v 2 : m 2 + m 4 – cover v 3 : m 3 + m 5 • Note that – match m 2 requires m 1 – match m 3 requires m 1 • This problem is intractable

Heuristic algorithms • Strategy: apply two preprocessing steps before covering, decomposition and partitioning • Decomposition – cast network and library in standard form – decompose into base functions – example: NAND2 and INV • Partitioning: – break network into cones – transform network from multiple-input multiple-output to many mutiple-input single-output subnetworks • Covering – cover each subnetwork by library cells

Decomposition

Partitioning

Covering

Structural matching and covering • Expression patterns – represented by dags • Identify pattern dags in network – sub-graph isomorphism • Simplification – use tree patterns

Example

Tree-based matching • Network – partitioned and decomposed 8 NOR2 (or NAND) + INV 8 generic base functions – each cone is called a subject tree • Library – represented by trees – possibly more than one tree per library cell • Can solve library binding problem with pattern recognition – simple binary tree match Disclaimer: lecture notes based on originals by Giovanni De Micheli

Simple library

Tree covering • Dynamic programming – visit subject tree from the bottom up (from the input variables up) • At each vertex – attempt to match 8 locally rooted subtree 8 all library cells • Optimum solution (e.g., to minimize area) for the subtree

Example

Example

Tree covering - labeling • For all vertices in the subject tree, the covering algorithm determines matches between locally rooted subtrees and pattern trees • Three possible conditions for any vertex and a given pattern tree: – the cell tree and the rooted subtree are isomorphic 8 the vertex is labeled with the cell cost – the cell tree is isomorphic to a subtree with leaves L 8 the vertex is labeled with the cell cost plus the labels of the vertices L – there is no match 8 cannot happen when the base functions are in the library

Example of labeling • Goal: minimum area cover • Area costs: – INV:2; NAND2:3; AND2:4; AOI21:6 • Best choice found: – AOI21 fed by a NAND2 gate

Example

Minimum delay cover • Dynamic programming approach • Cost related to gate delay • Delay modeling – constant gate delay 8 straightforward – load dependent delay: constant + load dependent term 8 load fanout unknown – for most libraries, values of input capacitance are finite and small 8 therefore, can use binning techniques, where we label each vertex with all possible load values

Minimum delay cover constant delays • Same labeling rules as the minimum area case • If the cell pattern tree and the rooted subtree are isomorphic, – the vertex is labeled with the cell delay • If the cell tree is isomorphic to a subtree with leaves L , each leaf already labeled, – the vertex is labeled with the cell cost plus the maximum of the labels of L

Example • Inputs are all ready at time 0, except for d , which arrives at time 6 • Constant delays: – INV:2; NAND2:4; AND2:5; AOI21:10 • Compute label for each vertex from the bottom up – labels t i are data-ready times – t x = 4; t y = 2; t z = 4; t w = 2; • Best choice: – AND2, two NAND2 and INV

Example

Minimum delay cover load dependent delays • Model: – assume a finite set of load values • Dynamic programming approach: – compute an array of solutions for each possible load – for each input to a matching cell, the best match for any load is selected • Optimum solution, when all possible loads are considered

Example • Input data-ready times are 0, except for t d = 6 • Load-dependent delays: – = oad at output – INV:1+ ; NAND2:3+ ; AND2:4+ ; AOI21:9+ • Loads: (for use with ) – INV:1; NAND2:1; AND2:1; AOI21:1 • If output load is also 1, the we find the same solution as before

Example • Input data-ready times are 0, except for t d = 6 • Add super inverter to library: larger area, less delay • Load-dependent delays: – INV:1+ ; NAND2:3+ ; AND2:4+ ; AOI21:9+ ; SINV:1+0.5 • Loads: – INV:1; NAND2:1; AND2:1; AOI21:1; SINV:2 • Assume output load is 1 – same solution as before • Assume output load is 5 – solution uses SINV cell

Example