Presentation
Hardware PDE Solvers Using Dynamic Stochastic Computing
DescriptionSubstantially different from an ordinary differential equation (ODE), a partial differential equation (PDE) contains partial derivatives over multiple independent variables. As a result, the computation complexity of PDEs increase dramatically. However, since PDEs are widely used in modeling natural and biological phenomenon, such as thermodynamics and fluid dynamics, it's necessary to compose efficient hardware PDE solvers while maintaining a high accuracy at the same time. In this paper, dynamic stochastic computing (DSC) is considered to implement PDE solvers with reduced circuit complexity. In a DSC-based implementation, a varying signal is encoded by a dynamic stochastic sequence (DSS) consisting of 0's and 1's. The numerical solutions are then obtained by operations on the stochastic bits from multiple DSS's instead of processing complex fixed-/floating-point numbers as in a conventional arithmetic circuit, thus significantly reducing the circuit area and power consumption. Basic stochastic circuits are proposed to provide unbiased estimates of the solutions for the heat and Burgers equations. When these basic circuits are connected in an array, they can solve a 2-D heat equation and a 1-D Burgers equation, respectively. The quality of the results produced by the proposed circuits is high. The RMSE is lower than 4.99*10e-3 when solving the heat equation, and lower than 1.840*10e-4 solving the Burgers equation, while up to 93.90% hardware and 97.09% power savings are achieved compared to fixed-point implementations.
Event Type
Work-in-Progress Poster
TimeWednesday, June 265:00pm - 6:00pm PDT
LocationLevel 2 Lobby
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