Automatic synthesis of physical system differential equation models to a custom network of general processing elements on FPGAs

Abstract

Fast execution of physical system models has various uses, such as simulating physical phenomena or realtime testing of medical equipment. Physical system models commonly consist of thousands of differential equations. Solving such equations using software on microprocessor devices may be slow. Several past efforts implement such models as parallel circuits on special computing devices called Field-Programmable Gate Arrays (FPGAs), demonstrating large speedups due to the excellent match between the massive fine-grained local communication parallelism common in physical models and the fine-grained parallel compute elements and local connectivity of FPGAs. However, past implementation efforts were mostly manual or ad hoc. We present the first method for automatically converting a set of ordinary differential equations into circuits on FPGAs. The method uses a general Processing Element (PE) that we developed, designed to quickly solve a set of ordinary differential equations while using few FPGA resources. The method instantiates a network of general PEs, partitions equations among the PEs to minimize communication, generates each PE's custom program, creates custom connections among PEs, and maintains synchronization of all PEs in the network. Our experiments show that the method generates a 400-PE network on a commercial FPGA that executes four different models on average 15x faster than a 3 GHz Intel processor, 30x faster than a commercial 4-core ARM, 14x faster than a commercial 6-core Texas Instruments digital signal processor, and 4.4x faster than an NVIDIA 336-core graphics processing unit. We also show that the FPGA-based approach is reasonably cost effective compared to using the other platforms. The method yields 2.1x faster circuits than a commercial high-level synthesis tool that uses the traditional method for converting behavior to circuits, while using 2x fewer lookup tables, 2x fewer hardcore multiplier (DSP) units, though 3.5x more block RAM due to being programmable. Furthermore, the method does not just generate a single fastest design, but generates a range of designs that trade off size and performance, by using different numbers of PEs.

Synthesis of networks of custom processing elements for real-time physical system emulation

“…The quality of the resulting partition (in terms of design size and cycles per step) is usually within 20% of our manually-obtained partitions. Some further details of the automatic partitioner are discussed in Huang et al [2012].…”

“…To accelerate physical model emulation, we previously introduced an approach using a network of processing elements (PEs) optimized for ODE solving [Huang et al 2012]. We first created a lightweight programmable ODE-solver PE for a field-programmable gate array (FPGA), where the PE was optimized for solving tens of ODEs [Huang et al 2011].…”

Synthesis of custom networks of heterogeneous processing elements for complex physical system emulation

“…We previously [7][8] proposed a homogeneous processing element network approach for physical system emulation, and built a compiler to convert a model specification to synthesizable VHDL code. The previous custom PE networks contained homogenous processing elements, either general PEs, or custom PEs, but not both.…”

“…We previously [7][8] proposed a network of homogeneous processing-elements on FPGAs approach for even faster emulation of a physical system. A set of lightweight custom processors each solve a subset of ODEs in the physical system.…”

“…Models with widely-varying structures can be efficiently implemented on FPGAs due to FPGA configurability. Initially, we used a custom network of general PEs [7], with each general PE able to solve any ODEs, yielding 10x-20x speedups over a single-core Intel I7 desktop processor running at 3.07 GHz. Later, we introduced a custom network of custom PEs [8], where a single custom PE was first created to best shown for illustration purpose match the needs of the model's ODEs and then a network of those custom PEs was used to achieve 5x-10x speedups over the network of general PEs.…”