Path-Based Scheduling

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Path-Based Scheduling
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Overview

• Motivation
• Introduction Prior work / new paradigm
• Defining problem and model constraints
• Solution
• AFAP Scheduling
• Algorithm
• The scheduling steps using AFAP
• Results
• Conclussion

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Motivation

• Derive a synthesis tool which
• Minimizes the number of control steps
• Takes constraints into account
• Considers loops and conditional branches
• ( which no prior work has done )

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Introduction

• Prior work scheduling lt- minimize cost function
• Cost function
• - Fixed hardware (uP) Number of states
• - Hardware syntheses Number of states
  hardware
• Scheduling
• Minimizing the cost function by moving
  operations around
• Optimal solution
• - Optimal schedule emphasizing concurrency
• - Force directed serialization moving mobile
  operations

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Introduction / Problem

• Path-Based Scheduling for Synthesis
• Forget cost functions!
• Instead Emphasize on conditional branches, loops
• Minimize number of control steps
• - taking constraints into account ( main
  problem )
• Main problem Gain advantages from looking at
  branches

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Constraints

• Internal constraints
• Units can only receive and output values once per
  cycle
• With single phase clock this implies single use
  per cycle
• External constraints
• Amount of hardware available
• Area, units etc.
• Timing constraints

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AFAP scheduling

• AFAP ( As Fast As Possible )
• Basic idea
• Control-flow directed graph- Nodes (ops.). Edges
  (precedence relations).
• Longest path Max number of operations. Cycles
  only traversed once (loop unfolding)
• Scheduling Put as many operations into one
  control step as possible in all possible paths.
• Goal Finite State Machine to implement control

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AFAP Scheduling for a single path

• AFAP Sch. for a single path ( no loops/branches )
• Longest path is computed
• For every path constraints are computed
  (variables, IOs, func. Units, max delay) and
  Cuts are lain in according to constraints
• Interval graph is formed with cliques (complete
  subgraph of all poss. edges)
• Cuts an cliques are stored for later processing

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AFAP Scheduling for a single path
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AFAP The algorithm

• The four steps to Nirvana ( or something )
• Transform the control flow graph into a directed
  acyclic graph (DAG)
• All paths in the DAG are scheduled AFAP
• Schedules are overlapped in a way to minimize the
  number of control steps
• The finite state machine is built.

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AFAP Example
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Building finite state machine

• Trivial finite state machine design
• Overlapping of intervals to form states (same cut
  -gt same state)
• Construct state transitions (figure out control
  signals for each state)
• Construct state transition conditions (rules for
  looping, waiting etc)

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Results

• Comparison is difficult since goals are
  different.
• Different compiler machines used

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Conclusion Good things / bad things

• Good things
• Real life examples
• Good results
• Bad things
• No support for secondary hardware constraints
• - like busses, registers, ports etc.
• No smart loop unfolding capabilities
• No pipeline scheduling capabilities
• No instruction execution reordering supported