By Abhik Roychoudhury M.S. and Ph.D. in Computer Science from the State University of New York at Stony Brook
ISBN-10: 0123742307
ISBN-13: 9780123742308
Modern embedded platforms require excessive functionality, comparatively cheap and occasional strength intake. Such structures normally encompass a heterogeneous number of processors, really good reminiscence subsystems, and in part programmable or fixed-function parts. This heterogeneity, coupled with concerns resembling hardware/software partitioning, mapping, scheduling, etc., ends up in various layout probabilities, making functionality debugging and validation of such structures a tough problem.
Embedded structures are used to manage safeguard serious functions reminiscent of flight regulate, car electronics and healthcare tracking. essentially, constructing trustworthy software/systems for such purposes is of extreme value. This e-book describes a bunch of debugging and verification tools that can aid to accomplish this goal.
- Covers the key abstraction degrees of embedded platforms layout, ranging from software program research and micro-architectural modeling, to modeling of source sharing and conversation on the approach level;
- Integrates formal innovations of validation for hardware/software with debugging and validation of embedded process layout flows;
- Includes sensible case stories to respond to the questions: does a layout meet its necessities, if no longer, then which components of the procedure are chargeable for the violation, and after they are pointed out, then how may still the layout be definitely changed?
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Sample text
How do we add communication to our basic FSM model? The simplest way is to label the transitions with “action” names. Thus, each FSM is now defined as M ϭ (S, I, ⌺, →), where S is a finite set of states, I ⊆ S is the set of initial states, ⌺ is a collection of actions also called the action alphabet, and →⊆ S ϫ ⌺ ϫ S is the transition relation. Composition of FSMs is now defined as follows. Definition 3 (Composition of Communicating FSMs) Given two FSMs M1 ϭ (S1 , I1 , ⌺1 , →1 ) and M2 ϭ (S2 , I2 , ⌺2 , →2 ) their composition is defined as M ϭ (S1 ϫ S2 , I1 ϫ I2 , ⌺1 ∪ ⌺2 , →) Here S1 ϫ S2 is the set of states, I1 ϫ I2 is the set of initial states, and ⌺1 ∪ ⌺2 is the action alphabet.
Sometimes, model simulations can be used to achieve more ambitious goals. Multiple simulations can be carried out through a systematic exploration of the model, possibly ensuring some sort of structural coverage of the model. By such multiple simulations we can generate a suite of execution runs or test cases that give a clear idea about the set of behaviors captured by the model. 1 FSM Simulations Simulating one single FSM representing global system behavior is not particularly difficult. After all, an FSM naturally comes equipped with execution semantics, and we can exploit this execution semantics to guide our simulation.
The overall control flow of the system is obtained by any arbitrary interleaving of the control flows of the constituent processes. Interleaving is implicitly integrated into the definition of FSM composition — a composite state (s, t) makes a transition if and only if either s or t makes a transition in their local FSMs. In a broad sense, having a notion of interleaving integrated in the process composition is a useful thing to do. In particular, consider the system implementation as implementing the local FSMs in a distributed fashion.