Ed Safford III MetroCon 2015 Verification, Validation, and Deployment of Hybrid Neuromorphic Systems Rev 5
- 1. 1 “Verification, Validation, and Deployment of Hybrid Neuromorphic Systems” E.L. (Ed) Safford III 10-22-2015
- 2. 2 Some Old Ideas Revisited Robotics Drones Pulse Codes – Spikes Nonlinear Stochastic Theory Cybernetics NOT NEW
- 3. 3 Past Limited by our machines, we have obtained operational capability by: a) Constrained Inputs (Information Loss) b) Simplified Computational Models c) Tasks Assigned to Machine V&V Has Been Relatively Mature for These Systems: a) Safety b) Security c) Mission Critical We put “Man-in-the-Loop” for Additional Adaptability and Assurance
- 4. 4 Future New Machines Will Provide Better Performance Systems Will Become Larger and More Complex Operational Demands Will Increase: a) Improved System Adaptability b) Greater Machine Autonomy c) Better Coverage of “Must Not”
- 5. 5 V&V Problem Assurance Methods Have Not Adequately Progressed to Support The Full Potential of These Systems For Deployment Therefore We Have Not Been Able to Delegate Certain Desired Levels of Operational Autonomy
- 6. 6 Idealized Brain Neural Network - from m1.behance.net
- 7. 7 Simplified Brain Neuron SYNAPSES are where Axon termini form junctions with Dendrites
- 8. 8 Neuron Model Weighted Inputs Outputs Activation Function Summation Neuron
- 9. 9 Artificial Neural Networks 1. Composed of one or more “layers” of “neurons”, i.e. neuron models, with nodes and connections 2. There is an input layer, an output layer, and one or more additional layers called “hidden” layers. May have feedback loops. 3. Mathematically can be represented by composition of functions with known or unknown parameters. DEs for feedback. Run Modes: Estimation and Approximation
- 10. 10 Diagram y = f(x) = WTσ(VTx) + ε(x) x0 = 1 x1 x2 xn yn y1 . . . . . . . . .
- 11. 11 Neuromorphic (NM) Systems “ … hardware specifically designed for machine learning or inspired by biological neural networks” (1) “Brain Inspired” or “brain-like” in its function (2) 1) Appavoo et al, “programmable Smart Machines: A Hybrid Neuromorphic approach to General Purpose Computation”, http://people.bu.edu/schuye/files/appavoo-neuroarch-2014.pdf 2) Mohda, D. S., “Introducing A Brain Inspired Computer”, refers to article published in Science 8 August 2014, http://www.research.ibm.com/articles/brain-chip.shtml
- 12. 12 Hybrid Neuromorphic Systems Can be very complex combinations of digital and analog devices (hardware and software) that include components which simulate or emulate neurological function in order to learn and perform the required tasks. A broader definition for neuromorphic computing, covering those systems which include both von Neumann (VN) and non-VN architectures. Calimera, Andrea et al, “The Human Brain Project and neuromorphic computing”,http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3812737/pd f/191-196.pdf
- 14. 14 • Henry Markram EPFL Lausanne, Switzerland An HBP Pilot (downselect 2012) Patch clamp study for model data • Neuroscience Modeling Uses IBM Blue Gene Computer Michael Hines’s NEURON sw Blue Brain 2005-2012
- 15. 15 SyNAPSE is backronym – Neuromorphic Adaptive Plastic Scalable Electronics DARPA Program to develop neuromorphic machine technology that scales to biological levels Substantial Funding went to HRL & IBM SyNAPSE
- 16. 16 Hewlett-Packard Research Lab (HRL) Dec 2011 CMOS chip - Simultaneous memory storage and logic processing - Multi-bit fully-addressable memory with up to 30 Gbits/sq cm MEMRISTOR Arrays
- 17. 17 Massively Parallel Cortical Simulation on Blue Gene/P (Dawn) Supercomputer at LLNL 2009 - 1.6 Billion Neurons, 8.87 Trillion Synapses - Spiking Neurons (STDP) and axon delay (0.1 ms simulated time step) IBM “Cat Brain” Sim C2
- 18. 18 Lab Prototype Digital Neurosynaptic Core Processor and Integration Board Announced Aug 2011 - 256 leaky integrate and fire (LIF) neurons on 45 nm SOI CMOS hw - deterministic hw with ~1 kHz chip clock (emulates ~1ms bio time-step) IBM New uProcessor Core
- 19. 19 Neuromorphic Architecture Using Simple LIF Spiking Neurons and “Binary” Synapses - May 2012 And arranged in layers like brain, BUT - Synaptic homeostatic renormalization - Burst spiking time dependent plasticity (Burst STDP) New Implementation and Models
- 20. 25 System Capabilities • Robots • - Baxter • - Brett • Big-Data-Mining Engines • Self-Completing Software • - PLINY, Brain#### • Autonomous Vehicles • - Google Cars • - Quadcopters • Natural Language Processing • Feature Recognition and Tracking
- 21. 26 Model-Based V&V “was” Good Enough Systems were simpler in their complexity and autonomy They could be white and black box tested at points of homeostasis And … “A Meta Model could be constructed that defined acceptable system states and behaviors” - Blackburn
- 22. 27 Then What Happened? Very Complex Learning Systems Dramatic Emergent Behavior
- 24. 29 A “Disruptive” System Technology Announcement And COMPASS TN Simulator June 2014
- 26. 31 • They Learn From a Teacher • They Learn From Each Other • They Learn From Their Environment • Deep Learning and Reservoir Computing Emergent Behaviors Must Be Controlled • New Methods of V&V are Needed to Achieve Desired Assurance Levels The Machines Learn
- 27. 32 Problem Re-Statement The (Stochastic) Complexity of the Systems We Are Now (and Will Continue to Be) Constructing and Deploying (e.g. Reservoir Computing and Liquid State Machines) Is Rapidly Exceeding Our Ability To Adequately and Feasibly Verify, Validate, and Control Them
- 28. 33 A neural network can be trained as a test “oracle” or model of the original system to determine whether a given test case exposes a fault or not. - Dale Boren A firm foundation for the use of neural networks in feedback control systems has been developed over the years by many researchers. - F. L. Lewis There Are Concepts
- 29. 34 1. Blackburn, M., Denno, P., Virtual Design and Verification of Cyber-Physical Systems: Industrial Process Plant Design, Procedia Computer Science, Volume 28, 2014, Pages 883-890, http://www.sciencedirect.com/science/article/pii/S187705 0914000696 There Are Studies
- 32. 37 Neurogrid - Stanford SpiNNaker (Spiking NN Arch.) - University of Manchester TrueNorth (Compass, SyNAPSE) - IBM There Are Platforms
- 35. 40 IT IS TIME For the Trainable Testbed