Impact of Hardware/Software Partitioning and MicroBlaze FPGA Configurations on the Embedded Systems Performances - Complex System Modelling and Control Through Intelligent Soft Computations - page 714

Table 4 Performance evaluation techniques
CPU benchmarks Synthetic benchmarks

Application based benchmarks
Algorithm based benchmarks

Performance measurement MP-on chip performance monitoring counters

Off-chip Hw monitoring
SW monitoring
Micro-coded instrumentation

Performance modeling Simulation Trance driven simulation

Execution driven simulation
Complete system simulation
Even driven simulation
Software profiling

Analytical model Probabilistic models

Queuing models
Markov models
Petri net models

them. Some research efforts are presented by Noonburg and Shen ( 1997 ) using a
Markov models to model a pipelined processor, when Sorin et al. ( 1998 ) used
probabilistic techniques to model a Multi-processor composed by superscalar
processors.

Simulation-Based Approach

Simulation-Based approach presents the best performance modeling method in the
performance evaluation of processor architectures. Model of the processor being
simulated must be written in a high-level language, such as C or Java and running
on some existing machine. Simulators give performance information in terms of
cycles of execution, cache bit ratios, branch prediction rates, etc. Many commercial
and academics simulators are presented: The SinOS simulator which presents a
simple pipeline processor model and a powerful superscalar processor model. The
SIMICS simulator simulates uni-processor and multi-processor models. Results of
simulation approaches are not very interested in the performance evaluation of the
MicroBlaze Xilinx soft-core processor because they are not exact.

5.1.2 Performance Measurement

Performance measurement approach is used for understanding systems that are
already built or prototyped. Two major purposes for performance measurement
approach can be used to tune systems to be built in order to understand the