Efficient Software Performance Estimation Methods for Hardware/Software Codesign

1
Efficient Software Performance Estimation Methods
for Hardware/Software Codesign

• Kei Suzuki
• Alberto Sangiovanni-Vincentelli
• Present Yanmei Li

2
Introduction

• One of the most important purposes of hw/sw
  codesign is to find the optimum hw/sw partition
  of a system level specification under particular
  criteria
• Criteria
• Performance(speed, or the number of clock cycles)
• Cost(number of components, die size, or code
  size)
• Estimation
• At a lower abstraction level
• easy and accurate, but long design iteration time
• At a higher abstraction level
• reduce the exploring time
• Play an important role in the synthesis and
  optimization

3
Software Performance Estimation

• Cost of a mixed hw/sw system based on a standard
  micro-processor depends on the hw size
• Solution Implement a given functionality with a
  program on the microprocessor
• Problem Software implementation often fails to
  meet the performance requirement
• Tradeoff
• To implement the critical portion in the program
  with hardware
• Software performance estimation is the key

4
POLIS System

• CFSMs (Codesign Finite State Machines)
• Does not discriminate between hw and sw
• Estimation provides preliminary timing
  information and also a measure for hw/sw
  partitioning
• A partitioning process takes place to identify
  the candidate components for sw implementation
• S-Graph (Software graph)
• To optimize the trade-off between the performance
  and the code size of the final implementation
• Estimation is helpful for s-graph optimization
  and sw module scheduling

5
Related Work

• Software performance depends on the structure of
  the software program as well as on the components
  of the target system
• The structure of the software program is more
  difficult to estimate as the abstraction level
  rises
• Most of the results are from the object code
  level which is the lowest level of abstraction,
  and are concerned with software that has a
  limited structure
• A number of approaches have been proposed
• A simple prediction method
• Statistical methods

6
Abstraction Models in POLIS

• CFSM
• HW be mapped into an abstract hardware
  description format, and synthesized into a
  combinational circuit and a set of latches
• SW be is translated into a data structure called
  s-graph

7
Abstraction Models in POLIS

• S-Graph
• A DAG(directed acyclic graph) with one source
  node and one sink node
• Represent the control flow of a given behavior
• Four types of node BEGIN, END, TEST, ASSIGN

8
S-Graph

• Semantics
• Start with the BEGIN node
• Traverse each node along its edge, until reaching
  the END node
• At a TEST node, select one corresponding child
  with the value of the associated predicate P(V)
• At an ASSIGN node, assign the value of the
  associated function A(V) to the output variable z
• Translate an s-graph into a C program
• Traverse the graph in a depth-first manner
• TEST if (or switch) statement
• ASSIGN assignment statement
• The resulting C program has the same structure

9
Performance Estimation Methods

• Modeling the target system
• The structure of C code generated by POLIS
• Function() (1)
• Initialization of local variable(assignment
  statements) (2)
• Structure of mixed if or switch statements and
  assignment statements (3)
• Return (4)

10
Modeling the Target System

• Execution time
• TTpp k Tinit Tstruct
• Code size
• SSpp k Sinit Sstruct
• Tpp (Spp) for entering and exiting the function
  (1)(4)
• Tinit ( Sinit)for initializing local
  variables(2).
• k is the number of local variables.
• Tstruct (Sstruct)for the structure of mixed
  conditional statements generated from TEST nodes
  and assignment statements generated from ASSIGN
  nodes(3).

11
Modeling the Target System(cont.)

• Tpp, Spp , Tinit , Sinit are constant which can
  be determined beforehand
• Tstruct SPi Ct (node_type_of(i),
  variable_type_of(i))
• Sstruct SCs (node_type_of(i), variable_type_of(i)
  )
• Pi 1 if node i is on a path, otherwise Pi 0
• Ct and Cs can be obtained by using simple
  benchmark programs containing a mix of the C
  statement that appears in the generated C
  programs and analyzing the execution time and
  code size of the programs on the target compiler
  and the target CPU

12
Benchmark Model

• Four attributes to characterize a system
• Name of the parameter set, a name for a unit of
  execution time, a name for a unit of code size,
  and the size of an integer variable
• seventeen cost parameters to model the execution
  time, and fifteen cost parameters to model the
  code size
• A TEST node with an event-type variable/multi-valu
  ed variable with a bit mask/multi-valued variable
  
• An ASSIGN node with an event-type variable/which
  assigns a constant to a variable/which assigns
  one variable to another one
• Pre-processing and post-processing
• A branch operation
• Initialization of a local variable
• Average execution time and size for pre-defined
  software library functions
• The size of pointers
• The size of integer variables

13
S-graph Level Estimation

• Property
• Property 1. Each node in an S-graph has a
  one-to-one correspondence with only a few
  statements in the synthesized C code
• Property 2. The form of each statement is
  determined by the type of corresponding node
• Property 3. The S-graph is a DAG, hence it does
  not include loops in its structure
• Each node/edge is weighted according to
  pre-calculated cost parameters in the pre-process

14
S-graph Level Estimation

• Algorithm SGtrace(sgi)
• If (sgiNULL) return (C(0,8,0))
• If(sgi has been visited)
• return (pre-calculated Ci(,,0) associated with
  sgi)
• Ciinitialize (max_time0 min_time8
  code_size0)
• For each child sgj of sgi
• CijSGtrace(sgj)edge cost for edge eij
• If(Cij.max_timegt Ci.max_time)
• Ci.max_time Cij.max_time
• If(Cij.min_timelt Ci.min_time)
• Ci.min_time Cij.min_time
• Ci.code_size Cij.code_size
• Ci node cost for node sgi
• Return(Ci)

15
S-graph Level Estimation

• The computational complexity O(E)
• Average execution time
• Cave SPij (Ct (node_type_of(i),
  variable_type_of(i)) Ce (i,j))
• Pij is the possibility of executing node i and
  going to node j
• Ce (i,j) is the edge cost for edge eij

16
CFSM Level Estimation

• Is much more difficult since a CFSM model does
  not closely reflect the code structure
• MDDs are used to represent the transition
  relation function of a CFSM (a node represents a
  multi-valued variable ordering is important)
• The estimation algorithm of the MDD is based on
  the assumption that the maximum(minimum) cost
  path in an MDD is usually the maximum (minimum)
  cost path in the s-graph that is generated from
  the MDD
• Also based on recursive DFS traversing algorithm
• There is no relation between the code size of the
  number of the MDD nodes

17
Experimental Results(1)
18
Experimental Results(2)
19
Experimental Results(3)

• Compared to an assembly-level analysis
• S-graph(Table 1)
• The differences in the maximum execution time are
  within (-10, 10)
• The differences in the minimum execution time are
  within (-20,20)
• The differences in code size are within
  (-20,20)
• CFSM(Table 2)
• The differences in the maximum execution time are
  within (-10,25)
• The differences in the minimum execution time are
  within (-20,20)

20
Conclusions

• S-graph level method
• provides an accurate estimation for all analysis
  the maximum and minimum execution time, and code
  size.
• It is a useful technique for optimization in
  software synthesis because of its accuracy.
• CFSM level method
• is less accurate than the s-graph estimation, but
  it is still accurate enough when estimating the
  maximum and minimum execution time.
• is important for automatic partitioning of CFSMs
  into hardware and software parts, and also for
  scheduler generation.

21
Conclusions

• Two software performance estimation methods for
  use with the POLIS hardware/software codesign
  system are proposed in this paper.
• S-graph level method
• CFSM level method
• The experimental results showed that the accuracy
  of both proposed methods is high enough for use
  in the POLIS system.