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A Methodology for Architecture Exploration of heterogeneous Signal Processing Systems Paul Lieverse, Pieter van der Wolf, Ed Deprettere, Kees Vissers.

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Presentation on theme: "A Methodology for Architecture Exploration of heterogeneous Signal Processing Systems Paul Lieverse, Pieter van der Wolf, Ed Deprettere, Kees Vissers."— Presentation transcript:

1 A Methodology for Architecture Exploration of heterogeneous Signal Processing Systems Paul Lieverse, Pieter van der Wolf, Ed Deprettere, Kees Vissers

2 Outline Problem definition Related work Basic principles Methodology SPADE Case study Conclusion

3 Problem Definition  Modern signal processing systems have a heterogeneous architecture: programmable components to offer various functions and support different standards for transmission, dedicated hardware blocks for cost and power consideration.  New design Methodology SPADE for architecture exploration of heterogeneous signal processing systems Starts from a set of target applications Results in the definition of an architecture capable of executing the applications within predefined constraints

4 Problem Definition cont’d  The design has to start with abstract yet executable models. Cost of model construction and model evaluation Flexibility to explore alternative architectures

5 Related Work  Application modeling: Models of computation  Synchronous Dataflow(SDF), Dataflow Process Networks, Kahn Process Networks.  Architecture modeling and performance analysis at system level  Polis, reactive systems  Chinook, embedded systems  RASSP, DSP systems  Quantitative analysis of architectures, by A.C.J.Kienhuis, limited to a specific class of dataflow architectures  In contrast, SPADE distinguishes between application models and architecture models, and supports explicit mapping  reusability

6 Basic Principles  The Y-Chart: a general scheme for the design of programmable architectures

7 Basic Principles cont’d  Workload and Resources  Computation, communication workload  Processing, communication, memory resources, etc  Applications and architectures are modeled separately  Trace-Driven Simulation: performance analysis  Applications: network of concurrent communicating processes  Trace: workload

8 SPADE  Application Modeling  Objective: expose parallelism, make communication explicit  Kahn Process Networks  The execution is deterministic  It fits nicely with signal processing applications  It allows programmers to easily combine communication primitives with control constructs  Application Programmers Interface (API) of SPADE  Read function  Write function  Execution function, to handle symbolic instruction Trace entries are generated by them.

9 SPADE cont’d  Architecture Modeling  Easy to construct. No need to model the functional behavior  Library of Generic building blocks, which are parameterized  Trace driven execution unit (TDEU), which interprets trace entries  Interfaces, which connects the I/O ports of TDEU to communication resource  Generic bus block

10 SPADE cont’d  Mapping.  Each process is mapped onto a TDEU. Can be many-to-one, but not one-to-many  Each process port is mapped one-to-one onto an I/O port

11 SPADE cont’d  Simulation  Concurrently simulate the application model and the architecture model in a single memory space  Performance Metrics  The building blocks contain collectors for performance metrics. Data is collected during simulation.

12 Case Study: An MPEG-2 Decoder  Starts with the C-code of the MPEG-2 video decoder  Step 1: partition the sequential program into a parallel Kahn Process Network using API functions  Step 2: Collect statistics of the workload for different MPEG sequences, by running the application in stand-alone mode  Step 3: construct model for a realistic architecture, TM-2700 MPEG architecture, using the blocks from the library  Step 4: Define a mapping  Step 5: Perform simulation. Identify the bottlenecks.

13 Conclusion  SPADE supports efficient exploration of heterogeneous signal processing architectures that must satisfy the workload demands of multiple target applications  Kahn API functions can be used to structure applications  A broad class of architectures can be modeled with the generic architecture blocks from the library  Trace-driven simulation is used for co-simulation. Simulation speed, about 20,000 cycles per second for a relatively complex design.  A number of architectures and mappings can be explored


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