BLINK: Bit-Sparse LSTM Inference Kernel Enabling Efficient Calcium Trace Extraction (ISLPED'20)
Channel: UCLA VAST
Date: 2020-08-03

Conference Presentation: ISLPED'20, Virtual, Aug 10-12, 2020.
BLINK: Bit-Sparse LSTM Inference Kernel Enabling Efficient Calcium Trace Extraction
Speaker: Zhe Chen (zhechen@ucla.edu)
Authors: Zhe Chen, Garrett J. Blair, Hugh T. Blair and Jason Cong.
Affiliation: Computer Science Department, Department of Psychology, UCLA

Abstract: Calcium imaging miniscopes are widely used for monitoring the activity of a large population of neurons in freely behaving animals in vivo. Conventional calcium image analyses are hard to meet the power and latency requirements for neurofeedback devices. In this presentation, we propose a customized calcium image processing pipeline based on the long short-term memory (LSTM) for calcium trace extraction with high energy efficiency and short latency. In addition, we propose a bit-sparse representation approach to improve the efficiency of the LSTM inference, and we designed the circuits for the bit-sparse LSTM inference kernel (BLINK) in a 28-nm technology. Evaluation shows that the proposed bit-sparse representation can reduce the circuit area and power consumption by over 38%. Finally, we implemented the proposed calcium image processing pipeline on the Ultra96 platform, and demonstrated the calcium trace extraction from up to 1024 cells with sub-ms latency on a single FPGA device.