The current trend in high performance and embedded computing consists of designing increasingly complex heterogeneous hardware architectures with non-uniform communication resources. In order to take hardware and software design decisions, early evaluations of the system non-functional properties are needed. These evaluations of system efficiency require high-level information on both the algorithms and the architecture. In state of the art Model Driven Engineering (MDE) methods, different communities have developed custom architecture models associated to languages of substantial complexity. This fact contrasts with Models of Computation (MoCs) that provide abstract representations of an algorithm behavior as well as tool interoperability. In this report, we define the notion of Model of Architecture (MoA) and study the combination of a MoC and an MoA to provide a design space exploration environment for the study of the algorithmic and architectural choices. An MoA provides reproducible cost computation for evaluating the efficiency of a system. A new MoA called Linear System-Level Architecture Model (LSLA) is introduced and compared to state of the art models. LSLA aims at representing hardware efficiency with a linear model. The computed cost results from the mapping of an application, represented by a model conforming a MoC on an architecture represented by a model conforming an MoA. The cost is composed of a processing-related part and a communication-related part. It is an abstract scalar value to be minimized and can represent any non-functional requirement of a system such as memory, energy, throughput or latency.