Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks

Ahmed Sanaullah,Chen Yang,Yuri Alexeev,Kazutomo Yoshii,Martin C. Herbordt

doi:10.1186/s12859-018-2505-7

Abstract

BackgroundReal-time analysis of patient data during medical procedures can provide vital diagnostic feedback that significantly improves chances of success. With sensors becoming increasingly fast, frameworks such as Deep Neural Networks are required to perform calculations within the strict timing constraints for real-time operation. However, traditional computing platforms responsible for running these algorithms incur a large overhead due to communication protocols, memory accesses, and static (often generic) architectures. In this work, we implement a low-latency Multi-Layer Perceptron (MLP) processor using Field Programmable Gate Arrays (FPGAs). Unlike CPUs and Graphics Processing Units (GPUs), our FPGA-based design can directly interface sensors, storage devices, display devices and even actuators, thus reducing the delays of data movement between ports and compute pipelines. Moreover, the compute pipelines themselves are tailored specifically to the application, improving resource utilization and reducing idle cycles. We demonstrate the effectiveness of our approach using mass-spectrometry data sets for real-time cancer detection.ResultsWe demonstrate that correct parameter sizing, based on the application, can reduce latency by 20% on average. Furthermore, we show that in an application with tightly coupled data-path and latency constraints, having a large amount of computing resources can actually reduce performance. Using mass-spectrometry benchmarks, we show that our proposed FPGA design outperforms both CPU and GPU implementations, with an average speedup of 144x and 21x, respectively.ConclusionIn our work, we demonstrate the importance of application-specific optimizations in order to minimize latency and maximize resource utilization for MLP inference. By directly interfacing and processing sensor data with ultra-low latency, FPGAs can perform real-time analysis during procedures and provide diagnostic feedback that can be critical to achieving higher percentages of successful patient outcomes.

Highlights

Real-time analysis of patient data during medical procedures can provide vital diagnostic feedback that significantly improves chances of success
We explore the application-aware optimization space for compute-bound Multi-Layer Perceptron (MLP) inference processors using our proposed Field programmable gate arrays (FPGA)-based architecture
Using mass spectrometry benchmarks for cancer detection, we show that FPGAs can outperform traditional computing technologies such as CPUs and Graphics processing unit (GPU) for real-time MLP applications

Summary

Introduction

Real-time analysis of patient data during medical procedures can provide vital diagnostic feedback that significantly improves chances of success. Unlike CPUs and Graphics Processing Units (GPUs), our FPGA-based design can directly interface sensors, storage devices, display devices and even actuators, reducing the delays of data movement between ports and compute pipelines. Using the collected datasets as training data, the system infers the typical EEG patterns in real-time. Pereira et al introduced an approach to use machine learning algorithms to decode variables of interest from fMRI data [14]. Li and Zhou proposed a semi-supervised learning algorithm, Co-Forest, that uses undiagnosed samples along with only a small amount of diagnosed ones as training datasets for CAD systems targeting breast cancer diagnosis [18]

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