Accelerated Analog Neuromorphic Hardware Johannes Schemmel

Kirchhoff Institute for Physics Chair of Prof. Karlheinz Meier Ruprecht-Karls University Heidelberg, Germany Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Motivation future computing based on biological information processing

understanding biological information processing

need model system to test ideas modeling possibilities:

•

numerical model represents model parameters as binary numbers

•

physical model : analog Neuromorphic Hardware represents model parameters as physical quantities :

can be combined to form a hybrid system

→ voltage, current, charge Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Physical Model Example : Continuous Time Integrating Membrane Model

Consider a simple physical model for the neuron’s cell membrane potential V: V(t)

R = 1/gleak Eleak

dV Cm  g leak (Eleak  V  dt DV [V]

gleak [S]

Cm [F]

(gV)/C [V/s]

Biology(*)

10-2

10-8

10-10

100

VLSI

10-1

10-6

10-13

106

Cm

Inherent speed gap: 106 Volt/second →

accelerated neuron model

(*) from Brette/Gerstner, J. Neurophysiology, 2005 Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Measured Example Membrane Voltage Traces

# of Synaptic inputs : 1 2 4

Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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More Neuronal Diversity : Adaptive-Exponential Integrate-and-Fire • 180 nm CMOS • 24 calibration parameters stored on analog floating gates

Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Single Spike Firing Modes of the AdEx VLSI Neuron

tonic spiking

transient spiking

Ruprecht-Karls-Universität Heidelberg

adaptation

Johannes Schemmel

Kirchhoff Institute for Physics

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Burst Firing Modes of the AdEx VLSI Neuron

regular bursting

initial burst

Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Spike-Time Comparison with Poisson Input

Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Six Groups of Neurons Firing in a Chain

Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Attractor Network

Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Boltzman Machine with Neural Sampling

𝜈𝑘 = 𝑝 𝑧𝑘 = 1 =

1 1 + exp(−𝑢𝑘 )

Büsing et al. (2011)

Petrovici & Bill et al. (2013) Petrovici et al. (2015) Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Boltzman Machine with Neural Sampling Using hardware-in-the-loop training to match target distribution:

𝜈𝑘 = 𝑝 𝑧𝑘 = 1 =

1 1 + exp(−𝑢𝑘 )

Büsing et al. (2011)

Petrovici & Bill et al. (2013) Petrovici et al. (2015) Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Boltzman Machine with Neural Sampling Using hardware-in-the-loop training to match target distribution:

𝜈𝑘 = 𝑝 𝑧𝑘 = 1 =

1 1 + exp(−𝑢𝑘 )

Büsing et al. (2011)

Petrovici & Bill et al. (2013) Petrovici et al. (2015) Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Aspects of Modelling Neurobiology : Diversity and Connectivity Biology:

CMOS based analog Neuromorphic hardware :

Diversity :

a multitude of neuron morphologies and electorphysiologies

• heavily parameterized circuits necessary • need calibration for quantitative matching Also implemented in Heidelberg:  multi-compartment  back-propagating action potential  dentridic spikes  gap junctions between neighboring neurons Planned: • NMDA plateau potentials • calcium spikes Not yet clear how to do it: • gap junctions beween distant neurons

Connectivity :

1011 neurons, 1015 synapses in Human Brain

physical model of synapse is about 100 µm2 approx. 400 million synapses fit on a silicon wafer → 2.5 million wafer needed simple simulator model needs O(1016) bytes

10.000 synapses per neuron on average

14k inputs per neuron demonstrated

Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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114.000 dynamic synapses

512 neurons (up to 14k inputs)

chip-to-chip communication network

Wafer-Scale Neuromorphic HW

Wafer Module Neuromorphic chip

48 FPGA wafer communication beneath PCBs heatsink

power supplies

host links Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Machine Room

3/22/2016 neuromorphic.eu

Aspects of Modelling Neurobiology : Time and Plasticity

Time :

Plasticity :

Biology:

Analog Neuromorphic Hardware :

continuous time operation

 physical model

relevant timescales range from ms to years

 accelerated model compresses years to hours and hours to seconds

precisely controlled delays

programmable delay circuits needed → even more memory

grows from single precursor cell

programmable topology, large amounts of memory

genome codes for complex and diverse plasticity rules

flexible synaptic plasticity has to be integrated into synapse model area is limited → hybrid model necessary

Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Complexity of Synaptic Plasticity is Key to Biological Intelligence Protein complex organization in the postsynaptic density (PSD) “Organization and dynamics of PDZ-domainrelated supramodules in the postsynaptic density” W. Feng and M. Zhang, Nature Reviews NS, 10/2009

Protein-protein interaction map (…) of post-synaptic density “Towards a quantitative model of the post-synaptic proteome” O Sorokina et.al., Mol. BioSyst., 2011,7, 2813–2823 Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Start with Simple Model : Spike Time Dependent Plasticity presynaptic membrane potential postsynaptic membrane potential

Dt = tpost – tpre time Dt > 0 |Dt| < tcorrelated Dt < 0 synapse strength decreases synapse strength increases long term depression long term potentiation

Biological Evidence

extracellular stimulation intracellular stimulation  long-term depression  long-term potentiation

change in excitatory postsynaptic potential

Graphs taken from: Theoretical Neuroscience by P. Dayan and L. Abbott, 2001

Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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An Example Using Spike-Time-Dependent-Plasticity

Spikey USB based neuromorphic system

T. Pfeil, A.-C. Scherzer, J. Schemmel and K. Meier, Neuromorphic Learning towards Nano Second Precision, Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN). Dallas, TX, USA: IEEE Press, 2013, pp. 869-873. Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Using Neuromorphic Hardware : From Networks to Experiments Mapping

PyNN script

(reordered connection matrix)

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() [...]

Configuration/Evaluation

Routing

(comparing connection matrix)

Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Hybrid Plasticity Problem : millions of parameters • network topology • neuron sizes and 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) Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Second Generation Neuromorphic ASIC : HICANN-DLS analog outputs

output amplifier RX data

synapse tl, tr, bl, br

SERDES channel 0

RX clk

SERDES channel 1

TX dat data

SERDES channel 2

TX clk

top ppu

L1 top

digital core logic

analog network core

SERDES channel 3

main PLL

extclk JTAG and reset

fast ADC

bottom ppu

vertical layer1 repeaters

L1 bot

L1 left

horizontal layer1 repeaters

L1 right

synthesized RTL mixed full custom

new component : digital plasticity processing units (ppu) Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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2nd 65nm Hybrid Plasticity Prototype FPGA based controller board

plasticity processor

neuron circuits Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

synapse array Kirchhoff Institute for Physics

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Plasticity : Hybrid Scheme Provides Flexibility SIMD Plasticity Processing Unit

ADC array parallel conversion of STDP readout

• analog correlation measurement in synapses • A/D conversion by parallel ADC • digital Plasticity Processing Units → full access to synapse weights → full access to configuration data Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Concept of Hybrid Plasticity Operation

Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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Measurement Results for Multiplicative STDP Rule

′ 𝜔+

Δ𝑡 = 𝜔 + 𝑏+ 𝜔max − 𝜔 exp − 𝑐+

Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

𝜔′

−

∆𝑡 = 𝜔 − 𝑏− 𝜔 exp − 𝑐− Kirchhoff Institute for Physics

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Measurements Demonstrating Possible STDP Rules Hebbian :

AntiHebbian :

• very early results using •

Asymmetric Sensitivity :

Bistable learning :

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 …

Publication currently under review: S. Friedmann, J. Schemmel et.al.:

“Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware System” Ruprecht-Karls-Universität Heidelberg

Johannes Schemmel

Kirchhoff Institute for Physics

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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!

The research leading to these results has received funding from the EU FP7 Framework Programme under grant agreement nos. 269921 (BrainScaleS), 243914 (Brain-i-Nets) and 604102 (HBP).