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NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy NEURAL NETWORKS FOR SENSORS AND MEASUREMENT SYSTEMS Part II Vincenzo.

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Presentation on theme: "NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy NEURAL NETWORKS FOR SENSORS AND MEASUREMENT SYSTEMS Part II Vincenzo."— Presentation transcript:

1 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy NEURAL NETWORKS FOR SENSORS AND MEASUREMENT SYSTEMS Part II Vincenzo Piuri University of Milan, Italy

2 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy OUTLINE Neural networks: what are they? Implementation of intelligent sensors and measuring systems System design: a comprehensive approach

3 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy NEURAL NETWORKS Paradigm often suited to describe non-linear computation when the algorithmic definition cannot be easily identified or is too complex but the solutions can be easily shown by examples

4 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy NEURAL NETWORKS (2) When they are NOT useful or suited ? to compute linear functions to compute non-linear functions which can be satisfactorily linearized with respect to envisioned application when there are known and efficient algorithms to solve the problem when too few examples of the desired behavior are available

5 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy BIOLOGICAL NEURAL NETWORKS Artificial Neural Networks are biologically inspired computational models

6 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy ARTIFICIAL NEURAL NETWORKS Computation is defined by configuring the parameters of the uncommitted neural model by means of a configuration procedure (learning) y x w synaptic weights neuron i output links

7 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy ARTIFICIAL NEURAL NETWORKS (2) To define the neural computation we must specify the neural model –neuron model –network topology the model dimensions the configuration procedure –configuration algorithm –training data set –validation data set

8 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy NEURON MODELS

9 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy NETWORK TOPOLOGIES feedforward Kohonen feedback and memory Hopfield

10 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy CONFIGURATION PROCEDURES supervised learning unsupervised learning x y ANN E y real world xy ANN auto-organization

11 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy OPERATING LIFE generalization evolving adaptation domainco-domain (learning) good generalization poor generalization (poor learning)

12 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy COMPOSITE SYSTEMS Systems composed by: –algorithmic components –neural networks to exploit the best features of each paradigm SOFT COMPUTING ALGORITHM SOFT COMPUTING ALGORITHM

13 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy IMPLEMENTATION Analog hardware Digital dedicated hardware Digital configurable hardware DSP processor + software General-purpose processor + dedicated software General-purpose processor + configurable software

14 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy ANALOG HARDWARE excellent performance configuration fixed at production time difficult control of parameter accuracy synaptic circuit neuron circuit neural network integrated analog circuit

15 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy ANALOG HARDWARE WITH DIGITAL WEIGHTS very high performance good discretized control of weights configuration fixed at production time difficult control of some parameter accuracy synaptic circuit integrated mixed circuit mixed-mode multiplier

16 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy DIGITAL DEDICATED ARCHITECTURES configurable weights very good discretized control of parameters good performance digital architecture digital integrated circuit

17 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy DIGITAL DEDICATED ARCHITECTURES

18 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy DIGITAL CONFIGURABLE HARDWARE (FPGA) configurable computation very good discretized control of parameters good performance

19 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy DSP PROCESSOR + DEDICATED SOFTWARE dynamically highly-configurable computation very good discretized control of parameters reasonable performance neural network custom software

20 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy GENERAL PURPOSE PROCESSOR + DEDICATED SOFTWARE dynamically highly-configurable computation very good discretized control of parameters sufficient performance neural network custom software C C++ VisualC Assembler

21 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy GENERAL PURPOSE PROCESSOR + CONFIGURABLE SOFTWARE high system flexibility dynamically highly-configurable computation very good discretized control of parameters limited performance neural network configurable software

22 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy SYSTEM DESIGN SPECIFICATIONS IMPLEMENTATION ACCURACY PERFORMANCE COST

23 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy SYSTEM DESIGN (2) Latency Accuracy Hardware cost SM CM SM

24 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy SYSTEM DESIGN (3) Fully Hardware Implementation

25 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy SYSTEM DESIGN (4) 050010001500 2.3 2.35 2.4 2.45 2.5 2.55 2.6 2.65 2.7 2.75 2.8 x 10 4 Number of instructions Sum of Squared Error (L b +N b )+L (L b +N b )+L with Input (L b +N b )+N with Input L b +N N b N b +L L b (L b +N b )+N Fully Software Implementation

26 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy HIGH-LEVEL DESIGN

27 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy HIGH-LEVEL DESIGN (2)

28 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy HIGH-LEVEL DESIGN (3)

29 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy HIGH-LEVEL DESIGN (4)

30 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy HIGH-LEVEL DESIGN (5)

31 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy HIGH-LEVEL DESIGN (6)

32 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy HIGH-LEVEL DESIGN (7)

33 NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy HIGH-LEVEL DESIGN (8)

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