Zhe Chen
Jason Cong
ABSTRACT
Miniaturized fluorescent calcium imaging miniscope has become a prominent technique in monitoring the activity of a large population of neurons in vivo. However, existing calcium image processing algorithms are developed for off-line analysis, and their implementations on general-purpose processors are difficult to meet the real-time processing requirement under constrained energy budget for closed-loop applications. In this paper, we propose the CANSEE, a customized accelerator for neural signal enhancement and extraction from calcium image in real time. The accelerator can perform the motion correction, the calcium image enhancement, and the fluorescence tracing from up to 512 cells with less than 1-ms processing latency. We also designed the hardware that can detect new cells based on the long short-term memory (LSTM) inference. We implemented the accelerator on a Xilinx Ultra96 FPGA. The implementation achieves 15.8x speedup and over 2 orders of magnitude improvement in energy efficiency compared to the evaluation on the multi-core CPU.
Index Terms
- CANSEE: Customized Accelerator for Neural Signal Enhancement and Extraction from the Calcium Image in Real Time
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