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In order to accelerate multidimensional DSP computations, using dedicated supercomputers or cluster computers is required in some circumstances, e.g., weather forecasting and military radars. Nevertheless, using supercomputers designated to simply perform DSP operations takes considerable money cost and energy consumption.
An example of such an application is processing digital photographs with software such as Photoshop. When the application requirement is real-time, DSP is often implemented using specialized or dedicated processors or microprocessors, sometimes using multiple processors or multiple processing cores.
Then, the output sample y(n) can be computed in terms of the inputs and the output sample y(n − M) such that there are M delay elements in the critical loop. These elements are then used to pipeline the critical loop by M stages so that the sample rate can be increased by a factor M. Consider the 1st-order IIR filter transfer function
Signals (perhaps from audio or video sensors) are constantly converted from analog to digital, manipulated digitally, and then converted back to analog form. Many DSP applications have constraints on latency; that is, for the system to work, the DSP operation must be completed within some fixed time, and deferred (or batch) processing is not ...
The DSP implementation in the folding algorithm is a Data flow graph(DFG), which is a graph composed of functional nodes and delay edges.. Another input for folding algorithm is folding set which is the function maps an operation unit of original DFG to an operation of transformed DFG with the number n <= N indicated the order of reused operation.
The following graph is another example showing the unfolding algorithm. Notice that, if there is the delay smaller than unfolding factor J, the J-unfolded DFG would create the edge with 0 delay but whose corresponding edge at original DFG may be the non-zero edge. Therefore, folding process is potential to create the 0-delay edge to increase ...
For example, elements can use real or integer values to simulate DSP functions or sampled data filters. Because the event-driven algorithm is faster than the standard SPICE matrix solution, simulation time is greatly reduced for circuits that use event-driven models in place of analog models.