Digital Signal Processing for Embedded Communications and Biomedical Systems

Transcription

1 igital Signal Processing for Embedded Communications and Biomedical Systems Keshab K. Parhi istinguished McKnight University Professor University of Minnesota, Minneapolis May 3,

2 OUTINE Communications Systems - Folding - Polar ecoders Biomedical Systems - Communication - Feature Computation and Classification - Monitoring IC Chip Security by PUF / 4/3/

3 Wireless Phone Timeline

4 3 rd -order IIR filter Folding Transformation See Parhi, VSI igital Signal Processing Systems, Wiley, 999 A possible folding set: A={A, A, A, A 3 }, M={M, M, M, M 3 } 3 / 4/3/

5 Folding Transformation (Cont d) Folded 3 rd -order IIR filter Multiple algorithm operations are time-multiplexed to a single functional unit Area reduction! 4 / 4/3/

6 Folding Transformation (Cont d) 6 th -order IIR filter (cascade of two 3 rd -order IIR filter) Also can be folded into multiplier and adder A possible folding set with interleaved ordering: A={A, A, A, A, A, A, A 3, A 3 }, M={M, M, M, M, M, M, M 3, M 3 } 5 / 4/3/

7 Folding Transformation (Cont d) Folded 6 th -order IIR filter More Pipelining -> ow-power, High-Speed Hierarchical Folding Algorithm: switch i switch i, i+ 6 / 4/3/

8 Advances in Coding Theory Turbo Codes PC Codes Polar Codes (Most Recent) / 4/3/

9 What are polar codes? Successive cancellation ist-decoding WiMax turbo WiMax PC ist + CRC-6 Systematic + ist + CRC-6 Broadcast channels Wiretap channels Point-to-point channels Arıkan introduced polar coding in his breakthrough paper. Polar codes have provably capacity-achieving capability. The are applicable in a diverse set of scenarios. E. Arıkan, Channel polarization: a method for constructing capacity- achieving codes for symmetric binary-input memoryless channels, IEEE Trans. on Inf. Theory, vol. 55, no. 7, pp , July 9. Plot from UCS Web link

10 Successive Cancellation (SC) decoding ( ( ( 3 ( 4 ( 5 ( 6 ( 7 ( Stage u u u u 3 4 u u u 3 4 u 4 Stage u u u u 5 6 û û 4 û 6 : Type II PE : Type I PE Stage 3 û û 5 û 3 û ( ) ( y ) ( y, u ) ( 5) 4 ( y, u ) ( 3) 3 ( y, u ) ( 7) 6 ( y, u ) ( ) ( y, u ) ( 6) 5 ( y, u ) ( 4) 3 4 ( y, u ) ( ) 7 û û 5 û 3 û 7 û û 6 û 4 û Successive cancellation (SC) is one of the most popular decoding algorithms. It is suitable for VSI implementation for the FFTlike structure. Type I PE: ( i) N i ( ) ( ) (, u i i N i )=(-) (, i i N i,, ) (, N y u N y u o u e N yn u, e ), Type II PE: (i-) N i ( i) N i i ( i) N i ( N y, u )=artanh{tanh[ N ( y, u, o u, e ) ] tanh[ N ( yn, u, e ) ]}.

11 SC decoding algorithm ( ( ( 3 ( 4 ( 5 ( 6 ( 7 ( Stage u u u u 3 4 u u u 3 4 u 4 Stage u u u u 5 6 û û 4 û Stage 3 û û 5 û 3 û ( ) ( y ) ( y, u ) ( 5) 4 ( y, u ) ( 3) 3 ( y, u ) ( 7) 6 ( y, u ) ( ) ( y, u ) ( 6) 5 ( y, u ) ( 4) 3 4 ( y, u ) ( ) 7 Successive cancellation (SC) is one of the most popular decoding algorithms. It is suitable for VSI implementation for the FFTlike structure. : Type II PE : Type I PE Type I PE: ( i) N i u i ( i) N i i ( i) N i N ( y, u )=(-) N ( y, u, o u, e ) N ( yn, u, e ), Type II PE: (i-) N i ( i) N i i ( i) N ( N y, u )=artanh{tanh[ N ( y, u, o u, e ) ] tanh[ N ( yn, u i, e ) ]}. û û 5 û 3 û 7 û û 6 û 4 û

12 SC decoding algorithm ( ( ( 3 ( 4 ( 5 ( 6 ( 7 ( Stage u u u u 3 4 u u u 3 4 u 4 Stage u u u u 5 6 û û 4 û Stage 3 û û 5 û 3 û ( ) ( y ) ( y, u ) ( 5) 4 ( y, u ) ( 3) 3 ( y, u ) ( 7) 6 ( y, u ) ( ) ( y, u ) ( 6) 5 ( y, u ) ( 4) 3 4 ( y, u ) ( ) 7 Successive cancellation (SC) is one of the most popular decoding algorithms. It is suitable for VSI implementation for the FFTlike structure. : Type II PE : Type I PE Type I PE: ( i) N i u i ( i) N i i ( i) N i N ( y, u )=(-) N ( y, u, o u, e ) N ( yn, u, e ), Type II PE: (i-) N i ( i) N i i ( i) N ( N y, u )=artanh{tanh[ N ( y, u, o u, e ) ] tanh[ N ( yn, u i, e ) ]}. û û 5 û 3 û 7 û û 6 û 4 û

13 SC decoding algorithm ( ( ( 3 ( 4 ( 5 ( 6 ( 7 ( Stage u u u u 3 4 u u u 3 4 u 4 Stage u u u u 5 6 û û 4 û Stage 3 û û 5 û 3 û ( ) ( y ) ( y, u ) ( 5) 4 ( y, u ) ( 3) 3 ( y, u ) ( 7) 6 ( y, u ) ( ) ( y, u ) ( 6) 5 ( y, u ) ( 4) 3 4 ( y, u ) ( ) 7 Successive cancellation (SC) is one of the most popular decoding algorithms. It is suitable for VSI implementation for the FFTlike structure. : Type II PE : Type I PE Type I PE: ( i) N i u i ( i) N i i ( i) N i N ( y, u )=(-) N ( y, u, o u, e ) N ( yn, u, e ), Type II PE: (i-) N i ( i) N i i ( i) N ( N y, u )=artanh{tanh[ N ( y, u, o u, e ) ] tanh[ N ( yn, u i, e ) ]}. û û 5 û 3 û 7 û û 6 û 4 û

14 SC decoding algorithm ( ( ( 3 ( 4 ( 5 ( 6 ( 7 ( Stage u u u u 3 4 u u u 3 4 u 4 Stage u u u u 5 6 û û 4 û Stage 3 û û 5 û 3 û ( ) ( y ) ( y, u ) ( 5) 4 ( y, u ) ( 3) 3 ( y, u ) ( 7) 6 ( y, u ) ( ) ( y, u ) ( 6) 5 ( y, u ) ( 4) 3 4 ( y, u ) ( ) 7 Successive cancellation (SC) is one of the most popular decoding algorithms. It is suitable for VSI implementation for the FFTlike structure. : Type II PE : Type I PE Type I PE: ( i) N i u i ( i) N i i ( i) N i N ( y, u )=(-) N ( y, u, o u, e ) N ( yn, u, e ), Type II PE: (i-) N i ( i) N i i ( i) N ( N y, u )=artanh{tanh[ N ( y, u, o u, e ) ] tanh[ N ( yn, u i, e ) ]}. û û 5 û 3 û 7 û û 6 û 4 û

15 SC decoding algorithm ( ( ( 3 ( 4 ( 5 ( 6 ( 7 ( Stage u u u u 3 4 u u u 3 4 u 4 Stage u u u u 5 6 û û 4 û Stage 3 û û 5 û 3 û ( ) ( y ) ( y, u ) ( 5) 4 ( y, u ) ( 3) 3 ( y, u ) ( 7) 6 ( y, u ) ( ) ( y, u ) ( 6) 5 ( y, u ) ( 4) 3 4 ( y, u ) ( ) 7 Successive cancellation (SC) is one of the most popular decoding algorithms. It is suitable for VSI implementation for the FFTlike structure. : Type II PE : Type I PE Type I PE: ( i) N i u i ( i) N i i ( i) N i N ( y, u )=(-) N ( y, u, o u, e ) N ( yn, u, e ), Type II PE: (i-) N i ( i) N i i ( i) N ( N y, u )=artanh{tanh[ N ( y, u, o u, e ) ] tanh[ N ( yn, u i, e ) ]}. û û 5 û 3 û 7 û û 6 û 4 û

16 SC decoding algorithm ( ( ( 3 ( 4 ( 5 ( 6 ( 7 ( Stage u u u u 3 4 u u u 3 4 u 4 Stage u u u u 5 6 û û 4 û Stage 3 û û 5 û 3 û ( ) ( y ) ( y, u ) ( 5) 4 ( y, u ) ( 3) 3 ( y, u ) ( 7) 6 ( y, u ) ( ) ( y, u ) ( 6) 5 ( y, u ) ( 4) 3 4 ( y, u ) ( ) 7 Successive cancellation (SC) is one of the most popular decoding algorithms. It is suitable for VSI implementation for the FFTlike structure. However, the decoding latency is (N-). N over are always required. : Type II PE : Type I PE Type I PE: ( i) N i ( ) ( ) (, u i i N i )=(-) (, i i N i,, ) (, N y u N y u o u e N yn u, e ), Type II PE: (i-) N i ( i) N i i ( i) N ( N y, u )=artanh{tanh[ N ( y, u, o u, e ) ] tanh[ N ( yn, u i, e ) ]}. û û 5 û 3 û 7 û û 6 û 4 û

17 SC decoding algorithm ( ( ( 3 ( 4 ( 5 ( 6 ( 7 ( Stage u u u u 3 4 u u u 3 4 u 4 Stage u u u u 5 6 û û 4 û Stage 3 û û 5 û 3 û ( ) ( y ) ( y, u ) ( 5) 4 ( y, u ) ( 3) 3 ( y, u ) ( 7) 6 ( y, u ) ( ) ( y, u ) ( 6) 5 ( y, u ) ( 4) 3 4 ( y, u ) ( ) 7 Successive cancellation (SC) is one of the most popular decoding algorithms. It is suitable for VSI implementation for the FFTlike structure. However, the decoding latency is (N-). N over are always required. : Type II PE : Type I PE Type I PE: How to reduce the latency? ( i) N i ( ) ( ) (, u i i N i )=(-) (, i i N i,, ) (, N y u N y u o u e N yn u, e ), Type II PE: (i-) N i ( i) N i i ( i) N ( N y, u )=artanh{tanh[ N ( y, u, o u, e ) ] tanh[ N ( yn, u i, e ) ]}. û û 5 û 3 û 7 û û 6 û 4 û

18 ata flow graph (FG) analysis ( ( ( 3 ( 4 ( 5 ( 6 ( 7 ( Stage u u u u 3 4 u u u 3 4 u 4 A B A B A 3 Stage 9 5 u u u u 5 6 B 3 A 4 û B C C 3 3 C C 4 4 û 4 û 6 : Type II PE : Type I PE Get the FG for it. Stage 3 û û 5 û 3 û E ( ) ( y ) E 3 ( 5) 4 y u (, ) E ( 3) y u (, ) E 4 ( 7) 6 y u (, ) F ( ) y u (, ) F 3 ( 6) 5 y u (, ) F ( 4) 3 y u (, ) F 4 ( ) 7 y u A A A 3 A 4 (, ) C C E F E F Marked each PE with red labels as indicated. The derived FG is singlerated. Now we are able to derive the decoder architectures. B B B 3 B 4 C C E F E F û û û 3 û 4 û 5 û 6 û 7 û

19 atency-reduced architecture First, we would like to derive a multi-rate version of the previous FG. Then, with the look-ahead manner, it can be further refined as follows. Stage 3 Stage E F end {,9} {5,} {3,6,,3} 4 {4,7,,4} C {} {} Stage A start B Stage 3 E/F {5} end Stage {3,6} {} C/ {4,7} {5} {4,7} 4 Stage A/B start C. Zhang, B. Yuan and K.K. Parhi, IEEE ICC

20 atency-reduced architecture Stage Stage Stage 3 Merged PEs are used instead of Type I and Type II PEs. The decoding latency is only 5% of tree architecture. Only half of the delay elements are employed by the feedback part. ( ( ) I ( y ) O 3 u u u u ( 3 ( 4 ( 5 ( 6 ( 7 ( I O O 3 4 I I I I I I u u u u u u û u 3 4 u 4 O O O 3 u O O O 3 u O O O 3 u 3 4 u 4 û 4 m I O O I O 3 I O O I O 3 u u or u u 5 6 u or u 6 u u or u u 6 u or u 5 6 m O I O O I 3 I O O I O 3 : Merged PE u i u i d

21 Additional architectures Based on the previous analysis on the FG, numerous decoder architectures can be obtained for different applications. Stage 3 and 3' u i u i O O O 3 I I Stage Stage u u or u u 5 6 O I 5 O u O I O I 3 y6 O u u Fully i O I O 4 O I O I 3 O I u pipelined 3 i y 7 O u or u 6 O I 3 y û 4 {5, } {3, 6} {4, } m outputs {5, } {3, 6} {4, 7} {5, } {3, 6} {4, 7} {5, } {3, 6} {4, 7} {5, } {3, 6} {4, 7} 3 U : Pipeline ( i ) i U U ( y, u ) y u d d d U U ( i) (, ) i O I O O I 3 signs Folded O I U3 {3, 4, 6, 7} {, 5} {3, 4, 6, 7} O O O 3 I I {, 5} {3, 4, 6, 7} O O O 3 O O O 3 I I I I ( ) ( y) O I ( ) 3 ( y) u u u u 3 4 u u 3 4 7l+,, 7 {5} {4} {6} {} {,, 7} {} {} {, 5} {} {, 5} O O O O O 3 I I I ( ( 3 ( 5 ( 6 ( 7 ( ( 4 ( ) ( y3) ( ) ( y4) ( ( ) ( ) ( ) ( ) ( ) ( ) FG u i u i {4, 5, 7, } {4, 5, 7, } {4, 5, 7, } {} O {, 3, 6} {} O {, 3, 6} {} O 3 {, 3, 6} {} O {, 3, 6} {} O {, 3, 6} {} O 3 {, 3, 6} {} {3, 6} O I {, } ( ) ( ) ( y ), ( y ) {, 3, 6} 5 3 Partial {} O {, 3, 6} {3, 6} u {} i parallel O I {, } ( ) ( ) 3 ( y ), ( y ) 6 4 {, 3, 6} {4, 5, 7, } {4, 5, 7, } {4, 5, 7, } {4,, } u {} i {, } ( ) ( ) O I ( y 7 ), ( y5 ) {, 3, 6} {} {3} O {4,, } {, 3, 6} {} {, } ( ) ( ) O I 3 ( y ), ( y ) 6 {, 3, 6} {3} d d U U U U U {4, 7} {5} U3 I I I I {4, 7} {5} l+3,, ( ) y ( ) y ( ), ( ) {, } 9 {3,, } ( ) y ( ) y ( ), ( ) {, } {, } ( ) y ( ) y ( ), ( ) 3 {3, 6} {, } ( ) y ( ) y ( ), ( ) 4 {3, 6} And more O {3, 6} {4} O 3 I ( c RAM U U

22 Body Area Network

23 Wireless Sensor Nodes in Healthcare

24 Wireless BAN

25 WBAN Applications Chronic disease monitoring Episodic patient monitoring Patients alarm monitoring Elderly people monitoring

26 Biomedical monitoring systems OFFINE TRAINING Recordings from atabases Feature extraction Feature selection Classifier Training ONINE ETECTION Selected feature set Classifier Model Electrodes Feature extraction Classification Postprocessing rug elivery System/ Create an alert Spectral power Wavelets Auto-regressive coefficients ICA inear SVM Non-linear SVM Adaboost Moving avearge Kalman

27 Closed-loop systems

28 MIMO Compared with the SISO case, channel capacity increases ~min{m,k} times by using a M x K antenna array.

29 Problem Statement - Transmitter Transmitted Signal Image face from pdrawinglessons/wpcontent/uploads///cartoonfacesh eads36degrees.png

30 Problem Statement Access Point Timing? (signal arrival time) Channel information? Carrier frequency offset? Received Signal

31 Solution Preamble Access Point

32 Contributions Achieve perfect timing synchronization when SNR db (% chance to find the correct timing) Existing methods only have 4% chance to find the correct timing at the same SNR Zero BER is achievable when SNR db Existing methods have error floors, and may not achieve zero BER at any SNR (SNR >> db) Te-ung Kung, Keshab Parhi, Optimized Joint Timing Synchronization and Channel Estimation for OFM Systems, IEEE Wireless Communications etters, on IEEExplore (Early Access). Te-ung Kung, Keshab Parhi, Frequency omain Symbol Synchronization for OFM Systems, IEEE EIT, May,.

33 Support Vector Machines Most widely used classification algorithm Training based on quadratic optimization Non-linear SVMs (kernel based) Map x to some high dimensional space The derived feature vectors are Kernel function allows implicit calculation of dot products earn a linear separator in high dimensional space T K( x, x ) ( x ) ( x ) The final prediction is i j i j ( x j ) : R d f ( x) a y ( x ) ( x) b a y K( x, x) b T i i i i i i i: a i: a i i

34 34 Illustration of SVM

35 Three popular kernels inear SVM Classification Polynomial K( x, x) i x x T ( ) T f x sign ai yixi x b sign w x b ia : i T i where w a y x i i i i T K( x, x) [ x x ] i i p p=: T K( x, x) ( z z) i i T T z zizi z where z [ x ] T

36 Polynomial SVM Classification T f ( x) sign z Wz b RBF Kernel i K( x, x) where W a y z z e x x i ia : i T i i i i f ( x) sign ai yik( xi, x) b ia : i Further simplifications not possible for RBF

37 Computational complexity Kernel #words (memory) #addition s inear d d d Polynomial (p=) d d d(d+) RBF N sv (d+) N sv d N sv d #multiplications * Additional N sv exponential operations for RBF Complexity depends on number of support vectors and # dimensions

38 Reducing the complexity Number of support vectors (N sv ) Reduced SVM (RSVM) can be used Number of SVs decrease while training Feature dimensionality (d) Feature selection algorithms SVM-RFE (Recursive Feature Elimination) Adaboost, HP, etc.,. Optimizing the hardware MAC and exponent operations Memory requirements depends on word length

39 39 Configurable SVM processor

40 4 SVM Architectures: Energy Consumption

41 FFT Architectures: Prior esigns 4-parallel delay-feedback M. Shin, et.al Contain 4 datapaths Yuan chen, et.al 4

42 IF esign 4-parallel feed-forward design (IF) Two datapaths, processing samples each Requires 3N/ delay elements Hardware utilization is % 4

43 IF esign -parallel feed-forward design 43

44 IT 4-parallel Architecture N-point FFT requires N-4 delay elements 4logN complex adders #multipliers depend on the algorithm No delays at this stage 44

45 IT -parallel Architecture Requires N- delay elements No delays at two stages 45

46 IT-point FFT Architecture Hardware complexity Complex adders: Complex multipliers: 4+.4 elay elements: 4 M. Ayinala, M. Brown, K.K. Parhi, IEEE Trans. VSI Systems, June (patent) M. Ayinala, K.K. Parhi, ACM Great akes Symp., Utah, May 46

47 Seizure prediction (a) Open-loop (b) Closed-loop 47

48 Seizure Prediction Objective: Patient-specific prediction of seizures (5 min ahead) from EEG signal (6 electrodes) Issues: unbalanced data, feature selection input pattern feature extrac tion System implementation details: features ~ power measured in 9 spectral bands for 4 differential channels. Total 4x6 = 36 features classifier ~ Adaboost with decision stumps X classifier decision (class label) Y. Park,. uo, K. Parhi, T. Netoff, Epilepsia, Oct. 4

49 EEG ata for Classification Parts of EEG data identified by medical experts: ictal, preictal (+), interictal(-) Preictal and interictal data used for classification Each data sample ~ sec moving window Preictal (Class +) At least -hour gap Interictal (Class -) 49

50 5 Seizure Prediction

51 5 Seizure Prediction

52 Physical Unclonable Functions (PUFs) It is estimated that as much as % of all high-tech products sold globally are counterfeit which leads to a conservative estimate of billion of revenue loss. [Guajardo et al, ] evice cloning Side-channel attack Security Challenges Computing devices are becoming physically exposed Adversaries may physically temper the devices and extract secret keys from non-volatile memory Software-only protections are not enough

53 What is PUF? Extract secret keys from complex physical objects ue to manufacturing process variations, no two Integrated Circuits even with the same layouts are identical Physical Objects PUF Process Variations Unpredictable Behavior Easy to Evaluate Hard to Clone Unclonable anti-counterfeiting marks for ICs!

54 Silicon MUX PUF Challenge Q Response G All the multiplexers are identically designed. Each challenge creates two paths through the circuit. The response is generated by the racing result of the two paths. No special fabrication needed.

55 Security Characteristics of PUFs Uniqueness: inter-chip variation Unclonability: randomness Unpredictability: hard to model Reliability Intra-chip variation Authentication robustness (add extra processing circuits, e.g., error correcting techniques)

56 Contributions ogically-reconfigurable PUFs (security) Systematic statistical analysis of (feed-forward) MUX PUFs Modified feed-forward path (reliability) Two-arbiter authentication scheme (reliability) [] Y. ao and K.K. Parhi, "Novel Reconfigurable Silicon Physical Unclonable Functions", Proc. of Workshop on Foundations of ependable and Secure Cyber-Physical Systems (FSCPS-)," pp. 3-36, Chicago, April [] Y. ao and K.K. Parhi, "Reconfigurable Architectures for Silicon Physical Unclonable Functions," Proc. of IEEE Int. Conference on Electro Information Technology, Mankato, May

57 ogically-reconfigurable PUFs Reconfigurable PUF circuit Alter the model of PUF circuit to update the challengeresponse behavior, instead of re-mapping the challenge and response through pre- and/or post-processing Several novel solutions, e.g., Reconfigurable feed-forward MUX PUF MUX and emux PUF (Challenge) PUF n Response Reconfigurable

58 Reconfigurable feed-forward MUX PUF Ideas: using reconfigurable feed-forward path Original MUX PUF can be modeled as a linear additive delay model Feed-forward path: add nonlinearity to MUX PUF, improve the security Three types of feed-forward path: Cascade, Overlap, Separate based on the beginning stage and the ending stage of the feed-forward path experimental results have shown that the inter-chip and intra-chip characteristics of the 3 types are different our statistical analysis has demonstrated that the mathematical models of the 3 types are different

59 Why reconfigurable PUFs? Reconfigurability is desirable:. Application needs: updatable authentication keys. Improving the security, as the challenge-response behaviors can be updated (against modeling attacks). Solutions for reconfigurablility Challenge-like Challenge-like Reconfigurable RO Silicon PUF FPGA based Vulnerable to attacks & Poor performance. The frequencies of ring oscillators are possible to be evaluated by attackers Hard to implement: lower level design detail, symmetrical routing

60 Conclusions Wireless communications systems for body area network will grow significantly Biomedical monitoring systems and drug delivery systems will grow ow-power SP for biomedical monitoring will grow IC Chip Security by PUFs for biomedical systems / 4/3/

61 Acknowledgements Chuan Zhang, Bo Yuan (Polar) Te-ung Kung (Wireless BAN) Manohar Ayinala (SVM, FFT) Yun-Sang Park, an uo, Prof. T. Netoff (Epilepsy) Yingjie ao (PUF) / 4/3/