Name
Title | ||
|---|---|---|
FPGA Architecture of Generalized Laguerre-Volterra MIMO Model for Neural Population Spiking Activities |
Abstract | ||
|---|---|---|
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 prethreshold 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*10^5 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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/FCCM.2011.21 | FCCM |
Keywords | Field | DocType |
maximum clock rate,fpga architecture,pipelined architecture,gradient calculation unit,quad core processor,efficient architecture,generalized laguerre-volterra mimo model,xilinx virtex-6 fpga,neural population spiking activities,amd pheono,different design speed requirement,error approximation function component,proposed architecture,computer model,field programmable gate arrays,computational modeling,field programmable gate array,hardware,neural nets | Population,Computer science,Convolution,Parallel computing,Field-programmable gate array,MIMO,Real-time computing,Software,Artificial neural network,Multi-core processor,Clock rate | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Will X. Y. Li | 1 | 45 | 7.08 |
| Ray C. C. Cheung | 2 | 625 | 72.26 |
| Wei Zhang | 3 | 440 | 72.00 |
| Rosa H M Chan | 4 | 182 | 22.79 |
| Dong Song | 5 | 202 | 34.25 |
| theodore w berger | 6 | 380 | 87.26 |