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Introduction to Sequential Circuits

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Introduction to Sequential Circuits
Some Slides from: U.C. Berkeley, Alan Mishchenko, Mike Miller, Gaetano Borriello

FSM (Finite State Machine) Optimization
State tables identify and remove equivalent states State minimization assign unique binary code to each state State assignment Combinational logic optimization use unassigned state-codes as don’t care net-list

Sequential Circuits Sequential Circuits
Primitive sequential elements Combinational logic Models for representing sequential circuits Finite-state machines (Moore and Mealy) Representation of memory (states) Changes in state (transitions) Basic sequential circuits Shift registers Counters Design procedure State diagrams State transition table Next state functions

State Assignment Choose bit vectors to assign to each “symbolic” state
With n state bits for m states there are 2n! / (2n – m)! state assignments [log n <= m <= 2n] 2n codes possible for 1st state, 2n–1 for 2nd, 2n–2 for 3rd, … Huge number even for small values of n and m Intractable for state machines of any size Heuristics are necessary for practical solutions Optimize some metric for the combinational logic Size (amount of logic and number of FFs) Speed (depth of logic and fanout) Dependencies (decomposition)

State Assignment Strategies
Possible Strategies Sequential – just number states as they appear in the state table Random – pick random codes One-hot – use as many state bits as there are states (bit=1 –> state) Output – use outputs to help encode states (counters) Heuristic – rules of thumb that seem to work in most cases No guarantee of optimality – an intractable problem

One-hot State Assignment
Simple Easy to encode, debug Small Logic Functions Each state function requires only predecessor state bits as input Good for Programmable Devices Lots of flip-flops readily available Simple functions with small support (signals its dependent upon) Impractical for Large Machines Too many states require too many flip-flops Decompose FSMs into smaller pieces that can be one-hot encoded Many Slight Variations to One-hot – “two hot”

Heuristics for State Assignment
Adjacent codes to states that share a common next state Group 1's in next state map Adjacent codes to states that share a common ancestor state Adjacent codes to states that have a common output behavior Group 1's in output map

General Approach to Heuristic State Assignment
All current methods are variants of this 1) Determine which states “attract” each other (weighted pairs) 2) Generate constraints on codes (which should be in same cube) 3) Place codes on Boolean cube so as to maximize constraints satisfied (weighted sum) Different weights make sense depending on whether we are optimizing for two-level or multi-level forms Can't consider all possible embeddings of state clusters in Boolean cube Heuristics for ordering embedding To prune search for best embedding Expand cube (more state bits) to satisfy more constraints

Output-Based Encoding
Reuse outputs as state bits - use outputs to help distinguish states Why create new functions for state bits when output can serve as well Fits in nicely with synchronous Mealy implementations

Example of KISS Format Inputs Present State Next State Outputs C TL TS ST H F 0 – – HG HG – 0 – HG HG – HG HY – – 0 HY HY – – 1 HY FG – FG FG – – FG FY – 1 – FG FY – – 0 FY FY – – 1 FY HG HG = ST’ H1’ H0’ F1 F0’ + ST H1 H0’ F1’ F0 HY = ST H1’ H0’ F1 F0’ + ST’ H1’ H0 F1 F0’ FG = ST H1’ H0 F1 F0’ + ST’ H1 H0’ F1’ F0’ HY = ST H1 H0’ F1’ F0’ + ST’ H1 H0’ F1’ F0 Output patterns are unique to states, we do not need ANY state bits – implement 5 functions (one for each output) instead of 7 (outputs plus 2 state bits)

Current State Assignment Approaches
For tight encodings using close to the minimum number of state bits Best of 10 random seems to be adequate (averages as well as heuristics) Heuristic approaches are not even close to optimality Used in custom chip design One-hot encoding Easy for small state machines Generates small equations with easy to estimate complexity Common in FPGAs and other programmable logic Output-based encoding Ad hoc - no tools Most common approach taken by human designers Yields very small circuits for most FSMs

State Assignment = Various Methods
Assign unique code to each state to produce logic-level description utilize unassigned codes effectively as don’t cares Choice for S state machine minimum-bit encoding log S maximum-bit encoding one-hot encoding using one bit per state something in between Modern techniques hypercube embedding of face constraint derived for collections of states (Kiss,Nova) adjacency embedding guided by weights derived between state pairs (Mustang)

Hypercube Embedding Technique
Observation : one -hot encoding is the easiest to decode Am I in state 2,5,12 or 17? binary : x4’x3’x2’x1x0’(00010) + x4’x3’x2x1’x0 (00101) + x4’x3x2x1’x0’(01100) + x4x3’x2’x1’x0 (10001) one hot : x2+x5+x12+x17 But one hot uses too many flip flops. Exploit this observation 1. two-level minimization after one hot encoding identifies useful state group for decoding 2. assigning the states in each group to a single face of the hypercube allows a single product term to decode the group to states.

FSM Optimization S2 S3 S1 S4 PI PO Combinational Logic v1 u1 NS PS v2
00 01 -0 S2 S3 0- 10 -1 11 01 -0 1- S1 S4 11 PI PO Combinational Logic v1 u1 NS PS v2 u2

State Group Identification
Ex: state machine input current-state next state output start S S S S S S S S start S start S S start S S S S S S S S S S S S S Symbolic Implicant : represent a transition from one or more state to a next state under some input condition.

Representation of Symbolic Implicant
Symbolic cover representation is related to a multiple-valued logic. Positional cube notation : a p multiple-valued logic is represented as P bits (V1,V2,...,Vp) Ex: V = 4 for 5-value logic (00010) represent a set of values by one string V = 2 or V = 4 (01010)

Minimization of Multi-valued Logic
Find a minimum multiple-valued-input cover - espresso Ex: A minimal multiple-valued-input cover

State Group Consider the first symbolic implicant 0 0110001 0000100 00
This implicant shows that input “0” maps “state-2” or “state-3” or “state-7” into “state-5” and assert output “00” This example shows the effect of symbolic logic minimization is to group together the states that are mapped by some input into the same next-state and assert the same output. We call it “state group” if we give encodings to the states in the state group in adjacent binary logic and no other states in the group face, then the states group can be implemented as a cube.

Group Face group face : the minimal dimension subspace containing the encoding assigned to that group. Ex: **0 group face 0100 0110

Hyper-cube Embedding c state groups : {2,5,12,17} {2,6,17} a b 12 17 6
wrong! 6 5 12

Hyper-cube Embedding c state groups : {2, 6, 17} {2, 4, 5} a b 6 17 2
wrong! 5

Hyper-cube Embedding Advantage :
use two-level logic minimizer to identify good state group almost all of the advantage of one-hot encoding, but fewer state-bit

Adjacency-Based State Assignment
Basic algorithm: (1) Assign weight w(s,t) to each pair of states weight reflects desire of placing states adjacent on the hypercube (2) Define cost function for assignment of codes to the states penalize weights for the distance between the state codes eg. w(s,t) * distance(enc(s),enc(t)) (3) Find assignment of codes which minimize this cost function summed over all pairs of states. heuristic to find an initial solution pair-wise interchange (simulated annealing) to improve solution

Adjacency-Based State Assignment
Mustang : weight assignment technique based on loosely maximizing common cube factors

How to Assign Weight to State Pair
Assign weights to state pairs based on ability to extract a common-cube factor if these two states are adjacent on the hyper-cube.

Fan-Out-Oriented (examine present-state pairs)
Present state pair transition to the same next state S1 S3 S2 \$\$\$ S1 S2 \$\$\$\$ \$\$\$ S3 S2 \$\$\$\$ Add n to w(S1,S3) because of S2

Fan-Out-Oriented present states pair asserts the same output S3 S1 \$/j
Add 1 to w(S1 , S3) because of output j

Fanin-Oriented (exam next state pair)
The same present state causes transition to next state pair. \$\$\$ S1 S2 \$\$\$\$ \$\$\$ S1 S4 \$\$\$\$ Add n/2 to w(S2,S4) because of S1 S1 S2 S4

Fanin-Oriented (exam next state pair)
The same input causes transition to next state pair. \$0\$ S1 S2 \$\$\$\$ \$0\$ S3 S4 \$\$\$\$ Add 1 to w(S2,S4) because of input i S1 S3 i i S2 S4

FSMs have no useful two-level
Which Method Is Better? Which is better? FSMs have no useful two-level face constraints => adjacency-embedding FSMs have many two-level face constraints => face-embedding

Summary Models for representing sequential circuits
Abstraction of sequential elements Finite state machines and their state diagrams Inputs/outputs Mealy, Moore, and synchronous Mealy machines Finite state machine design procedure Deriving state diagram Deriving state transition table Determining next state and output functions Implementing combinational logic Implementation of sequential logic State minimization State assignment Support in programmable logic devices

Some Tools History: Combinational Logic  single FSM  Hierarchy of FSM’s VIS (“handles” hierarchy) Facilities for managing networks of FSMs Sequential Circuit Optimization (single machine) MISII SIS Sequential Circuit Partitioning Facilities for handling latches

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