Digital Signal Processing (DSP) is becoming increasingly widespread in portable devices. Due to harsh constraints on power, latency, and throughput in embedded environments, developers often appeal to signal processing experts to hand-optimize algorithmic aspects of the application. However, such DSP optimizations are tedious, error-prone, and expensive, as they require sophisticated domain-specific knowledge.<\/p>\n
We present a general model for automatically representing and optimizing a large class of signal processing applications. The model is based on linear state space systems. A program is viewed as a set of filters, each of which hasan input stream, an output stream, and a set of internal states. At each time step, the filter produces some outputs that are a linear combination of the inputs and the state values; the state values are also updated in a linear fashion. Examples of linear state space filters include IIR filters and linear difference equations.<\/p>\n
Using the state space representation, we describe a novel set of program transformations, including combination of adjacent filters, elimination of redundant states and reduction of the number of system parameters. We have implemented the optimizations in the StreamIt compiler and demonstrate improved generality over previous techniques.<\/p>\n","protected":false},"excerpt":{"rendered":"
Digital Signal Processing (DSP) is becoming increasingly widespread in portable devices. Due to harsh constraints on power, latency, and throughput in embedded environments, developers often appeal to signal processing experts to hand-optimize algorithmic aspects of the application. However, such DSP optimizations are tedious, error-prone, and expensive, as they require sophisticated domain-specific knowledge. 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