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

Abstract

Real-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. We 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 proposedmore » FPGA design outperforms both CPU and GPU implementations, with an average speedup of 144x and 21x, respectively. In 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.« less

Authors:

Sanaullah, Ahmed ^[1]; Yang, Chen ^[1]; Alexeev, Yuri ^[2]; Yoshii, Kazutomo ^[2]; Herbordt, Martin C. ^[1]

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+ Show Author Affiliations

1. Boston Univ., MA (United States)
2. Argonne National Lab. (ANL), Argonne, IL (United States)

Publication Date:

Fri Dec 21 00:00:00 EST 2018

Research Org.:

Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Org.:

National Science Foundation (NSF); USDOE

OSTI Identifier:

1493450

Grant/Contract Number:  

AC02-06CH11357

Resource Type:

Accepted Manuscript

Journal Name:

BMC Bioinformatics

Additional Journal Information:

Journal Volume: 19; Journal Issue: S18; Journal ID: ISSN 1471-2105

Publisher:

BioMed Central

Country of Publication:

United States

Language:

English

Subject:

60 APPLIED LIFE SCIENCES