By Patrick R. Schaumont
This publication offers a scientific advent to the subject of Hardware-Software Codesign. the cloth emphasizes the elemental rules, and the sensible points of Hardware-Software Codesign. The ebook constructed from a direction with regards to Hardware-Software Codesign, prepared through the writer at Virginia Tech. it's separated into 4 differenct sections; easy options, customized Architectures, Hardware/Software Interfaces, and purposes. the writer covers many thoughts together with many of the different types of expressing computations, sequential and parallel implementations, control-flow and data-flow, keep watch over dependency and information dependency, latency and throughput in addition to the structure layout area of information paths, finite nation machines, micro-programmed machines, instruction-set processors, system-on-chip, and on-chip buses. the cloth additionally contains the several kinds of hardware/software interfaces, their impression on functionality, fee, and software program complexity. The publication comprises details on hardware/software integration of parts on most sensible of hardware/software interfaces in addition to layout technique and layout flows for hardware-software codesign together with functionality overview, verification and synthesis of and software program implementations. difficulties are incorporated on the finish of every bankruptcy and a options guide might be to be had for teachers.
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Extra resources for A Practical Introduction to Hardware/Software Codesign
The changes on those nodes will be delayed by one or more gate propagation-delays. Discrete-event simulation is very popular to model hardware at low abstraction level. It gets rid of the differential equations and the complexity of continuous-time simulation, yet it captures all relevant information such as glitches and clock cycle edges. Discrete-event simulation is also used to model systems at high abstraction level, to simulate abstract event with irregular spacing in time. For example, discrete-event simulation can be used to model customer queues at a bank.
In this case, the different functions of the system are mapped as individual entities or actors such as FileSource, Map, PulseShape, and DA. These actors are linked through communication channels or queues. The numbers at the input and output of each actor indicate the relative rate of communications. For example, there are 16 samples 36 2 Data Flow Modeling and Implementation produced by Map for each input sample. Each actor is an independent unit. It continuously checks its input for the availability of data, and as soon as data appear, calculates the corresponding output, and sends the result to the next actor in the chain.
3, there are two tokens in the upper queue, one with value ‘1’ and one with value ‘4’. The ‘4’ token was entered first into the queue, the ‘1’ token was entered after that. When the ‘add’ actor will read a token from that queue, the actor will first read the token with value ‘4’ and then the token with value ‘1’ (Fig. 4). 1 4 add 8 5 Fig. 3 Data flow model of an addition = actor 1 1 4 12 add 5 = token = queue fire! 8 add 5 1 12 add 6 fire! 5 Fig. 4 Actor firing moves a data flow model through markings add 12 38 2 Data Flow Modeling and Implementation When a data flow model executes, the actors will read tokens from their input queues, transform the values of the input tokens into output values, and generate new tokens on their output queues.