FPGA Architecture of Generalized Laguerre-Volterra MIMO Model for Neural Population Spiking Activities

We present a parallelized and pipelined architecture for a generalized Laguerre-Volterra MIMO system to identify the time-varying neural dynamics underlying spike activities. The proposed architecture consists of a first stage containing a vector convolution and MAC (Multiply and Accumulation) component, a second stage containing a pre-threshold potential updating unit with an error approximation function component, and a third stage consisting of a gradient calculation unit. A flexible and efficient architecture that can accommodate different design speed requirements are generated. Simulation results are rigorously analyzed. A hardware IP library for versatile models and applications is proposed. The design runs on a Xilinx Virtex-6 FPGA and the processing core produces data samples at a maximum clock rate of 357MHz, which is 4.37 × 10⁵ times faster than the corresponding software model running on an AMD Pheono 9750 Quad Core Processor. It occupies 216,766 LUTs, maximum 12 block-RAMs, and 2016 DSP-blocks.