FPGA Deployment of LFADS for Real-time Neuroscience Experiments
Xiaohan Liu ChiJui Chen YanLun Huang LingChi Yang Elham E Khoda Yihui Chen Scott Hauck Shih-Chieh Hsu Bo-Cheng Lai
Large-scale recordings of neural activity are providing new opportunities to
study neural population dynamics. A powerful method for analyzing such
high-dimensional measurements is to deploy an algorithm to learn the
low-dimensional latent dynamics. LFADS (Latent Factor Analysis via Dynamical
Systems) is a deep learning method for inferring latent dynamics from
high-dimensional neural spiking data recorded simultaneously in single trials.
This method has shown a remarkable performance in modeling complex brain
signals with an average inference latency in milliseconds. As our capacity of
simultaneously recording many neurons is increasing exponentially, it is
becoming crucial to build capacity for deploying low-latency inference of the
computing algorithms. To improve the real-time processing ability of LFADS, we
introduce an efficient implementation of the LFADS models onto Field
Programmable Gate Arrays (FPGA). Our implementation shows an inference latency
of 41.97 $\mu$s for processing the data in a single trial on a Xilinx U55C.