Kirchhoff Institute for Physics Johannes Schemmel Ruprecht-Karls-Universität Heidelberg 1 Accelerated Neuromorphic Hardware : Hybrid Plasticity - The Next Generation Johannes Schemmel Human Brain Project Subproject Neuromorphic Computing Neuromorphic Computing with Physical Models

Kirchhoff Institute for Physics Johannes Schemmel Ruprecht-Karls-Universität Heidelberg 2 Overview Overview of the NM-PM1 system Modeling with the NM-PM1 system Hybrid Plasticity NM-PM2 – HICANN DLS Prototype Results NM-PM : Neuromorphic Computing with Physical Models

Kirchhoff Institute for Physics Johannes Schemmel Ruprecht-Karls-Universität Heidelberg 3 Physical Model Example : Continuous Time Integrating Membrane Model  V [V] g leak [S]C m [F]  gV)/C [V/s] Biology(*) VLSI Consider a simple physical model for the neuron’s cell membrane potential V: CmCm R = 1/g leak E leak V(t) (*) from Brette/Gerstner, J. Neurophysiology, 2005 Inherent speed gap: 10 6 Volt/second → accelerated neuron model

Kirchhoff Institute for Physics Johannes Schemmel Ruprecht-Karls-Universität Heidelberg 4 More Neuronal Diversity : Adaptive-Exponential Integrate-and-Fire 180 nm CMOS calibration parameters stored on analog floating gates 180 nm CMOS calibration parameters stored on analog floating gates

Kirchhoff Institute for Physics Johannes Schemmel Ruprecht-Karls-Universität Heidelberg 5 Example Membrane Voltage Traces of HICANN V4 # of Synaptic inputs : 1 2 4

Kirchhoff Institute for Physics Johannes Schemmel Ruprecht-Karls-Universität Heidelberg 6 Six Groups of Neurons Firing in a Chain

Kirchhoff Institute for Physics Johannes Schemmel Ruprecht-Karls-Universität Heidelberg 7 Wafer Module wafer beneath heatsink power supplies 48 FPGA communication PCBs host links network chip

Kirchhoff Institute for Physics Johannes Schemmel Ruprecht-Karls-Universität Heidelberg 8 More Neuronal Diversity : Adaptive-Exponential Integrate-and-Fire 180 nm CMOS calibration parameters stored on analog floating gates 180 nm CMOS calibration parameters stored on analog floating gates Machine Room

Kirchhoff Institute for Physics Johannes Schemmel Ruprecht-Karls-Universität Heidelberg 9 Using NM-PM1 : From Networks to ExperimentsMapping import pyNN.stage2 as pynn pynn.setup() neuronParams = { 'v_init' : -70.6, 'w_init' : 0.0, [...] } pool0 = pynn.create(pynn.EIF_[...]) pool1 = pynn.create(pynn.EIF_[...]) [...] pynn.connect(pool0, pool0, p=0.26, weight=0.5) pynn.connect(pool1, pool0, p=0.16, weight=0.5) [...] pynn.run() [...] import pyNN.stage2 as pynn pynn.setup() neuronParams = { 'v_init' : -70.6, 'w_init' : 0.0, [...] } pool0 = pynn.create(pynn.EIF_[...]) pool1 = pynn.create(pynn.EIF_[...]) [...] pynn.connect(pool0, pool0, p=0.26, weight=0.5) pynn.connect(pool1, pool0, p=0.16, weight=0.5) [...] pynn.run() [...] PyNN script (reordered connection matrix) Routing Configuration/Evaluation (comparing connection matrix)

Kirchhoff Institute for Physics Johannes Schemmel Ruprecht-Karls-Universität Heidelberg 10 Hybrid Plasticity Problem : millions of parameters network topology neuron sizes and AdEx-parameters synaptic strengths Current status : everything is pre-computed on host-computer requires precise calibration of hardware takes long time (much longer than running the experiment on the accelerated system) Integrate flexible plasticity mechanisms : “Hybrid Plasticity” no calibration of synapses necessary plastic topology and delays learning replaces calibration combination of analog correlation measurement and digital Plasticity Processing Unit (PPU)

Kirchhoff Institute for Physics Johannes Schemmel Ruprecht-Karls-Universität Heidelberg 11 Second Generation Neuromorphic ASIC : HICANN-DLS analog network core bottom ppu top ppu digital core logic fast ADC vertical layer1 repeaters horizontal layer1 repeaters SERDES channel 0 output amplifier main PLL SERDES channel 1 SERDES channel 2 SERDES channel 3 analog outputs TX data TX clk RX clk RX data extclk JTAG and reset TX dat L1 top L1 right L1 left L1 bot synapse tl, tr, bl, br

Kirchhoff Institute for Physics Johannes Schemmel Ruprecht-Karls-Universität Heidelberg 12 Plasticity : Hybrid Scheme Provides Flexibility analog correlation measurement in synapses A/D conversion by parallel ADC digital Plasticity Processing Units → full access to synapse weights → full access to configurationdata SIMD Plasticity Processing Unit ADC array parallel conversion of STDP readout

Kirchhoff Institute for Physics Johannes Schemmel Ruprecht-Karls-Universität Heidelberg 13 NM-PM2 Prototype plasticity processor synapse array neuron circuits FPGA based controller board

Kirchhoff Institute for Physics Johannes Schemmel Ruprecht-Karls-Universität Heidelberg 14 Concept of Hybrid Plasticity Operation Synapse measures time-difference between pre- and post synaptic signals Time-difference is exponentially weighted Results are accumulated within each synapse for causal and anti-causal correlations separately Accumulated correlation measures are digitized PPU uses digitized values together with current weights to calculate new weight

Kirchhoff Institute for Physics Johannes Schemmel Ruprecht-Karls-Universität Heidelberg 15 Measurement Results for Multiplicative STDP Rule

Kirchhoff Institute for Physics Johannes Schemmel Ruprecht-Karls-Universität Heidelberg 16 Measurements Demonstrating Possible STDP Rules Hebbian : Anti- Hebbian : Asymmetric Sensitivity : Bistable learning : very early results using only variations of the STDP PPU code PPU also supports : supervised plasticity reinforcement learning including neuron firing rates in plasticity rules adding additional digital synaptic state variables anything you can code …

The research leading to these results has received funding from the EU FP7 Programme under grant agreement nos (BrainScaleS) and (HBP). This endeavor would not have been possible without the tireless commitment of all the involved students and colleagues, which unfortunately are too many to name them all here individually. Thank You!