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Karlheinz meier - How neuromorphic computing may affect our future life HBP Colloquium FZ Jülich, October 2018

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Brains of smarter people have bigger and faster neurons
Scientists working within the Human Brain Project have for the first time uncovered a direct relation between brain cell size and IQ level. As they describe in the journal eLife, larger neurons in the so-called temporal lobe of the brain that generate electrical signals with higher speed are related to faster processing rates and intelligence level as assessed in standard IQ testing.

https://www.humanbrainproject.eu/en/follow-hbp/news/brains-of-smarter-people-have-bigger-and-faster-neurons/

HBP research contributes to breakthrough neurotechnology for treating paralysis
Three patients with chronic paraplegia were able to walk again thanks to precise electrical stimulation of their spinal cords via a wireless implant. Scientists led by Grégoire Courtine (EPFL, CHUV/Unil) this month published these results in Nature and Nature Neuroscience. In the HBP the researchers developed personalized computational models for each patient that guided the placement of the electrode arrays over the spinal cord. They also study basic mechanisms of electrical spinal cord stimulation in a virtual experiment setup on the Neurorobotics Platform.

https://www.humanbrainproject.eu/en/follow-hbp/news/hbp-supports-breakthrough-neurotechnology-for-treating-paralysis/

https://www.humanbrainproject.eu/en/science/highlights-and-achievements/

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Karlheinz meier - How neuromorphic computing may affect our future life HBP Colloquium FZ Jülich, October 2018

  1. 1. From biology to your coffee machine ? How neuromorphic computing may affect our future life HBP Colloquium FZ Jülich, October 2018 Karlheinz Meier Ruprecht-Karls-Universität Heidelberg meierk@kip.uni-heidelberg.de @brainscales
  2. 2. No more progress from smaller transistors New ARCHITECTURES suddenly interesting ! First : Make use of CMOS devices Then : Pave the way for non-CMOS Brain-inspired, brain-derived or neuromorphic computing Definition Transferring aspects of structure and function from biological substrates to electronic circuits Structure : Cells – Networks – Connections Function : Local Processing – Communication – Learning K. Boahen, 2017
  3. 3. Assets of brain inspired computing Ø Energy efficiency Ø Compactness Ø Fault tolerance Ø Speed Ø Configuration and learning replace programming Ø Scalability © Larry Smarr, Calit Conventional computing is moving away from the brain
  4. 4. Neuromorphic : why and how ? Future computing based on biological information processing Understanding biological information processing Two fundamentally different modeling approaches • SYMBOLIC MODEL (Turing) represents model parameters as symbols (binary numbers) represents function as instructions (algorithms) • PHYSICAL MODEL (non Turing) represents model parameters as physical quantities (like the brain) represents function as circuit dynamics (physical laws) Combined into hybrid system Requires model system to test principles
  5. 5. © Intel Corporation The BrainScaleS Project Not to scale Same transistors - where‘s the neuron ?
  6. 6. Jordan, Jakob, et al. Extremely scalable spiking neural network simulation code: from laptops to exascale computers. Frontiers in Neuroinformatics, 2018, 12. Jg., S. 2. The simulation approach Weak scaling (sizeup) on modern petascale many-processor machines
  7. 7. Neuromorphic implementations : Towards biological realism Many-core (ARM) architecture Optimized spike communication network Programmable local learning x0.01 real-time to real-time Full-custom-digital neural circuits No local learning (TrueNorth) Programmable local learning (Loihi) Exploit economy of scale x0.01 real-time to x100 real-time Analog neural cores Digital spike communication Biological local learning Programmable local learning x10.000 real-time TrueNorth Biological realism Loihi Ease of use by traditional programming tools
  8. 8. The HBP Neuromorphic Computing Strategy 2nd generation emerged from co-design process in HBP 1st generation SpiNNaker-1 Machine Many-core system 1 Million ARM cores Real-time simulator 1st generation BrainScaleS-1 Machine Physical model system 4M neurons, 1B plastic synapes Accelerated emulator Towards the 2nd generation SpiNNaker-2 144 Cortex M4F per chip 36 GIPS/Watt per chip x10 performance with constant power Towards the 2nd generation BrainScaleS-2 On-chip plasticity processors Flexible hybrid plasticity Active dendrites Common software ecosystem, remote access, open user facility Close cooperation with (theoretical) neuroscience, application domain QPE QPE SpiNNaker Router Shared SRAM Host IF Chip2ChipIO Chip2ChipIO Many-core architecture SIMULATION These are neuromorphic processors designed and built from the transistor up ! Physical model EMULATION
  9. 9. Assets of brain inspired computing ? Ø Energy efficiency Ø Compactness Ø Fault tolerance Ø Speed Ø Configuration and learning replace programming Ø Scalability
  10. 10. BrainScaleS-2 65 nm prototype chip in the lab 2nd generation – HBP made Ø Hybrid plasticity with on-chip processor (PPU): on-chip loops, time-scales from ms to years § Input : timing correlations, rates, membrane potentials, external signals § Change : synaptic weights, neuromodulation, network structure Ø Structured neurons • Multicompartment neurons • Active, non-linear dendrites, backpropagating APs • NMDA, Ca plateau potentials Ø Public evaluation system by end-2018 Ø Full-size prototypes by mid-2020 Ø Full size system by 2023 Ø Funding pending
  11. 11. Aamir, S. A. et al. (2018). An Accelerated LIF Neuronal Network Array for a Large Scale Mixed-Signal Neuromorphic Architecture. IEEE Transactions on Circuits and Systems I: Regular Papers , 1 14 In-the-the-loop experiments on silicon – External computers only for set-up Wunderlich et al., 2018, to be submitted to Frontiers of Neuromorphic Engineering Compactness
  12. 12. Wunderlich et al., 2018, to be submitted to Frontiers of Neuromorphic Engineering PONG : Closed action- perception loop • Reinforcement learning by reward- modulated spike-timing-dependent plasticity • R-STDP : three-factor learning rule that modulates the effect of unsupervised STDP using a reward signal • Inspired by the activity of dopamine in the brain found to encode expected reward (e.g. Hollerman and Schultz, 1998)
  13. 13. 220 µs 13 µJ 180 µs 10 µJ 200 ms O(pJ) 2.3 ms 55 mJ 50 ms 1.2 J Times and energies for single experiment iterations Energy measured directly through procesdsor curent Simulation running on one single core of an Intel i7-4771 CPU BSS2 : Chip Simulation : NEST Biology : Real time Speed and energy Wunderlich et al., 2018, to be submitted to Frontiers of Neuromorphic Engineering
  14. 14. Learning success in software (NEST) with and without artificial noise and hardware with intrinsic noise Exploiting noise Wunderlich et al., 2018, to be submitted to Frontiers of Neuromorphic Engineering
  15. 15. Weight matrix on chip after 105 iterations Variabiluity after learning represents self-calibration Learning replaces calibration Wunderlich et al., 2018, to be submitted to Frontiers of Neuromorphic Engineering Learning replaces calibration
  16. 16. BrainScaleS-2 Next generation neuromorphic computing Hybrid architecture analog-custom digital-processor nased Two on-chip plasticity processors Synaptic plasticity Neuronal plasticity Structural plasticity Active analog dendrites Multicompartment dendritic trees NMDA, Ca and Na spikes Scalability
  17. 17. Coffee machines ? More general : Distributed sensing / IoT IoT Challenges - Energy consumption - Size - Data transfer - Local preporcessing - Adaptation NMC technology - Energy efficiency - Compactness - Local intelligence (learning) - Adaptability All enabled by biological priciples
  18. 18. Concrete examples - Personal medical devices (EEG, ECG, drug delivery, nrve stimulation, brain implants, sensory substituation) - Intelligent adaptive control (engines, manufacturing plants) Project evaluation under way with BASF, Huawei, Airbus, Daimler Why do we need big HBP systems, then ? - Rapid prototyping (like LTL, continuous learning, one-shot learning, dendritic computation) - Neural FPGA concept

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