Low-Latency In Situ Image Analytics With FPGA-Based Quantized Convolutional Neural Network

Real-time in situ image analytics impose stringent latency requirements on intelligent neural network inference operations. While conventional software-based implementations on the graphic processing unit (GPU)-accelerated platforms are flexible and have achieved very high inference throughput, they are not suitable for latency-sensitive applications where real-time feedback is needed. Here, we demonstrate that high-performance reconfigurable computing platforms based on field-programmable gate array (FPGA) processing can successfully bridge the gap between low-level hardware processing and high-level intelligent image analytics algorithm deployment within a unified system. The proposed design performs inference operations on a stream of individual images as they are produced and has a deeply pipelined hardware design that allows all layers of a quantized convolutional neural network (QCNN) to compute concurrently with partial image inputs. Using the case of label-free classification of human peripheral blood mononuclear cell (PBMC) subtypes as a proof-of-concept illustration, our system achieves an ultralow classification latency of 34.2 mu s with over 95% end-to-end accuracy by using a QCNN, while the cells are imaged at throughput exceeding 29,200 cells/s. Our QCNN design is modular and is readily adaptable to other QCNNs with different latency and resource requirements.

Automated summary

Real-time in situ image analytics require stringent latency requirements on intelligent neural network inference operations. This study demonstrates that high-performance reconfigurable computing platforms based on FPGA processing can bridge the gap between low-level hardware processing and high-level intelligent image analytics algorithm deployment. The proposed design achieves ultralow classification latency with high accuracy for label-free classification of human PBMC subtypes.