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Accelerating Anomaly Detection Algorithms on

FPGA-Based High-Speed NICsHiroki Matsutani

Dept. of ICS, Keio Universityhttp://www.arc.ics.keio.ac.jp/~matutani

August 2nd, 2018 International Forum on MPSoC for Software-Defined Hardware (MPSoC'18) 1

http://en.wikipedia.org/wiki/Image:Keio-logo.png

http://en.wikipedia.org/wiki/Image:Keio-logo.png

Accelerator design for big data

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Input stream data Message queuing

RealtimeView

Database layer(Polyglot persistence)

DB queries

Data exchange (serialization)

Customer analysis Topic prediction Blockchain records Geolocation query …

Big data (Surveillance, Network service, SNS,UAV, IoT)

BatchView

BatchLearning

Inference

Batchprocessing

Streamprocessing

Today’s talk: Online learning

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RealtimeView


DB queries




BatchView

BatchLearning

I/O intensive Compute intensive

Message queuing middleware

(Apache Kafka)

Stream processing (Apache Spark

Streaming)

Batch processing(Apache Spark)

Batch learning (Apache Spark

MLlib)Serialization

(Apache Thrift)KVS / Column DB(Redis, HBase)

Document DB(MongoDB)

Graph DB (Neo4j), graph

processing

Inference(DNN, CNN, RNN)

Tight integration of I/O and compute FPGA

Massive parallelism Networked GPU cluster

GPUsHostSwitchFour

10GbE FPGA

Inference,Online

learning

Batchprocessing

Streamprocessing

Online learning(SGD, ChangeFinder,

OS-ELM)

Offline vs. Online learning

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All training data

New training

data+

Predictor

OK or NG?Test data

GPU-based batch processing

Examples: DNN, CNN, … Learning cost is high Predictor updated infrequently

Learning cost is low Predictor updated frequently Not very versatile

Predictor Test data ≒Training data

Sequential learning+ Inference

FPGA NIC/Switch

10GbEx4

FPGA-based stream processing

OK or NG?

Offline learning Online learning

Online learning approaches

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Next value Xt is predictedbased on recent p values

Online sequential learning for SLFN (input, hidden, and output layers)

Time t

Xt-1

Xt-2

Xt-3Xt-4

Xt-5

?

Xt= β1Xt-1 + β2Xt-2 + …

ChangeFinder:Outlier and change point detections on time-series data

AR-model based Neural network

ChangeFinder on 10GbE FPGA• ChangeFinder algorithm

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[J.Takeuchi, IEEE TKDE'06]

Elapsed time

Input data Xt

Change-point score

Step 1 (Outlier score):Receive input data Xt at time tCalculate outlier score of Xt based on past dataInfluence of past data controlled by discount rate r

Step 2 (Smoothing):Calculate moving average Yt of the outlier scoreSmoothing is controlled by window size S

Step 3 (Change-point score):Step 1 is performed for YtThe result is change-point score

ChangeFinder on 10GbE FPGA• ChangeFinder algorithm

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[J.Takeuchi, IEEE TKDE'06]

Elapsed time

Input data Xt

Change-point score

Threshold

Step 1 (Outlier score):Receive input data Xt at time tCalculate outlier score of Xt based on past dataInfluence of past data controlled by discount rate r

Step 2 (Smoothing):Calculate moving average Yt of the outlier scoreSmoothing is controlled by window size S

Step 3 (Change-point score):Step 1 is performed for YtThe result is change-point score

ChangeFinder on 10GbE FPGA• 10GbE NIC datapath by Verilog HDL• Application logic in wrapper in HLS

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10GMACRX 0

DMARX

Input Arbiter

Output PortLookup

BRAM OutputQueues

10GMACRX 3

10GMACTX 0

10GMACTX 3

DMATX

Wrapper

ChangeFinder.c

AXI4-Stream

AXI4-Stream

[T.Iwata, HeteroPar‘18]

ChangeFinder on 10GbE FPGA• Throughput: 83.4% of 10GbE line rate

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Youtube Video:https://www.youtube.com/watch?v=wgTcBfkE5hY

Online learning approaches

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Time t

Xt-1

Xt-2

Xt-3Xt-4

Xt-5

?

Xt= β1Xt-1 + β2Xt-2 + …

Next value Xt is predictedbased on recent p values

Online sequential learning for SLFN (input, hidden, and output layers)

n N m

Weight vector β00~β(N-1)(m-1)

ChangeFinder:Outlier and change point detections on time-series data

OS-ELM：Single hidden layer neural network (SLFN)

AR-model based Neural network

[N. Liang, TNN 2006]

Online learning + unsupervised

Pre-trained predictor

OK or NG?Test data Learning

predictorTest data ≒Training data

Sequential learning+ Inference

Offline learning Online learning

Inference only

Normal values (incl. noise) are learned after the deployment Anomaly detection adapted to a given environment

[M.Tsukada, HeteroPar’18]*Collaboration with Prof. M.Kondo (UTokyo)

+Unsupervised anomaly detection

(No training data needed)

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Online learning + unsupervised• Learn vibration pattern of fan + noise

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Youtube Video:https://www.youtube.com/watch?v=tCw7p7bjwTs

Summary: Online learning FPGA

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RealtimeView


DB queries




BatchView

BatchLearning

I/O intensive Compute intensive

Message queuing middleware

(Apache Kafka)

Stream processing (Apache Spark

Streaming)

Batch processing(Apache Spark)

Batch learning (Apache Spark

MLlib)Serialization

(Apache Thrift)KVS / Column DB(Redis, HBase)

Document DB(MongoDB)

Graph DB (Neo4j), graph

processing

Inference(DNN, CNN, RNN)

Tight integration of I/O and compute FPGA

Massive parallelism Networked GPU cluster

GPUsHostSwitchFour

10GbE FPGA

Inference,Online

learning

Batchprocessing

Stream processing

Online learning(SGD, ChangeFinder,

OS-ELM)

References (1/2)• Outlier detection on 10GbE FPGA NIC

– Ami Hayashi, et.al., "An FPGA-Based In-NIC Cache Approach for Lazy Learning Outlier Filtering", PDP 2017.

– Ami Hayashi, et.al., "A Line Rate Outlier Filtering FPGA NIC using 10GbE Interface", ACM Comp Arch News (2015).

• Change-point detection on FPGA NIC– Takuma Iwata, et.al., "Accelerating Online

Change-Point Detection Algorithm using 10GbE FPGA NIC", HeteroPar 2018.

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References (2/2)• Online sequential unsupervised anomaly

detector on FPGA– Mineto Tsukada, et.al., "OS-ELM-FPGA: An

FPGA-Based Online Sequential Unsupervised Anomaly Detector", HeteroPar 2018.

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Thank you !

Acknowledgement:This work is supported by JST CREST JPMJCR1785

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