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Feasibility analysis of real-time physical modeling using WaveCore processor technology on FPGA

Verstraelen, Math; Pfeifle, Florian and Bader, Rolf 

WaveCore is a scalable many-core processor technology. This technology is specifically developed and optimized for acoustical modeling applications. The programmable WaveCore soft-core processor is in principle silicon-technology independent and hence can be targeted to ASIC or FPGA technologies. The WaveCore programming methodology is based on dataflow principles and the abstraction level of the programming language is close to the mathematical structure of for instance finite-difference time-domain schemes. The instruction set of the processor inherently supports delay-lines and data-flow graph constructs. Hence, the processor technology is well suitable to capture both digital waveguide as well as finite-difference oriented algorithm descriptions. We have analysed the feasibility of mapping 1D and 2D finite-difference models onto this processor technology, where we took Matlab reference code as a starting point. We analyzed the scalability and mapping characteristics of such models on the WaveCore architecture. Furthermore we investigated the composability of such models, which is an important property to enable the creation and mapping of complete musical instrument models. One part of the composability analysis has been the combination of digital waveguide (FDN reverberation model) with finite-difference time-domain models (primitive six-string instrument model). Our main conclusion is that WaveCore is a promising technology for this application domain. The mapping experiments show a high efficiency in terms of FPGA utilization, combined with a programming methodology that matches in a transparent way with the mathematical abstraction level of the application domain. We used a standard FPGA board to get full-circle confidence of the carried-out analysis, as well as WaveCore compiler and simulator results to show the scalability of the processor technology to support large models.