With increasingly more complex Multi-Processor Systems on Chip (MPSoC) and shortening time-to-market projections, Transaction Level Modeling and Platform Aware Design are seen as promising approaches to efficient MPSoC design. In this paper, we present an automatized 3-phase process of Platform Aware Design and apply it to Kahn Process Networks (KPN) applications, a widely used model of computation for data-flow applications. We start with the KPN application and an abstract platform template and automatically generate an executable TLM with estimated timing that accurately reflects the system platform. We support homogeneous and heterogeneous multi-master platform models with shared memory or direct communication paradigm. The communication in heterogeneous platform modules is enabled with the transducer unit (TX) for protocol translation. TX units also act as message routers to support Network on Chip (NoC) communication. We evaluate our approach with the case study of the H.264 Encoder design process, in which the specification compliant design was reached from the KPN application in less than 2 hours. The example demonstrates that automatic generation of platform aware TLMs enables a fast, efficient and error resilient design process.
This paper presents a tool for automatic synthesis of RTL interfaces for heterogeneous MPSoC from transaction level models (TLMs). The tool captures the communication parameters in the platform and generates interface modules called universal bridges between buses in the design. The design and configuration of the bridges depend on several platform components including heterogeneity of the components, traffic on the bus, size of messages and so on. We define these parameters and show how the synthesizable RTL code for the bridge can be automatically derived based on these parameters. We use industrial strength design drivers such as an MP3 decoder to test our automatically generated bridges for a variety of platforms and compare them to manually designed bridges on different quality metrics. Our experimental results show that performance of automatically generated bridges are within 5% of manual design for simple platforms but surpasses them for more complex platforms. The area and RTL code size is consistently better than manual design while giving 5 orders of improvement in development time.
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