accelerated analog neuromorphic hardware -...
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Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 1
Accelerated AnalogNeuromorphic Hardware
Johannes Schemmel
Kirchhoff Institute for PhysicsChair of Prof. Karlheinz Meier
Ruprecht-Karls UniversityHeidelberg, Germany
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 2
Motivation
future computing based on biological information
processing
understanding biological information processing
modeling possibilities:
• numerical model
represents model parameters as binary numbers
• physical model :
analog Neuromorphic Hardware
represents model parameters as physical quantities :
→ voltage, current, charge
can becombined toform a hybrid system
need model system to test ideas
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 3
Physical Model Example : Continuous Time Integrating Membrane Model
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
Consider a simple physical model for the neuron’s
cell membrane potential V:
( VEgdt
dVC leakleakm
Cm
R = 1/gleak
Eleak
V(t)
(*) from Brette/Gerstner, J. Neurophysiology, 2005
Inherent speed gap:106 Volt/second
→ accelerated neuron model
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 4
Measured Example Membrane Voltage Traces
# of Synaptic inputs : 12 4
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 5
More Neuronal Diversity : Adaptive-Exponential Integrate-and-Fire
• 180 nm CMOS• 24 calibration parameters stored on
analog floating gates
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 6
Single Spike Firing Modes of the AdEx VLSI Neuron
tonic spiking
transient spiking adaptation
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 7
Burst Firing Modes of the AdEx VLSI Neuron
regularbursting
initial burst
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Spike-Time Comparison with Poisson Input
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Six Groups of Neurons Firing in a Chain
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Attractor Network
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 11
𝜈𝑘 = 𝑝 𝑧𝑘 = 1 =1
1 + exp(−𝑢𝑘)
Boltzman Machine with Neural Sampling
Büsing et al. (2011)
Petrovici & Bill et al. (2013)
Petrovici et al. (2015)
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 12
𝜈𝑘 = 𝑝 𝑧𝑘 = 1 =1
1 + exp(−𝑢𝑘)
Boltzman Machine with Neural Sampling
Büsing et al. (2011)
Petrovici & Bill et al. (2013)
Petrovici et al. (2015)
Using hardware-in-the-looptraining to matchtarget distribution:
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 13
𝜈𝑘 = 𝑝 𝑧𝑘 = 1 =1
1 + exp(−𝑢𝑘)
Boltzman Machine with Neural Sampling
Büsing et al. (2011)
Petrovici & Bill et al. (2013)
Petrovici et al. (2015)
Using hardware-in-the-looptraining to matchtarget distribution:
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 14
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 matchingAlso implemented in Heidelberg: multi-compartment back-propagating action potential dentridic spikes gap junctions between neighboring neuronsPlanned:• NMDA plateau potentials• calcium spikesNot 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 neededsimple simulator model needs O(1016) bytes
10.000 synapses per neuron on average
14k inputs per neuron demonstrated
Wafer-ScaleNeuro-
morphicHW
114.000 dynamic synapses
512 neurons (up to 14k inputs)
chip
-to
-ch
ip c
om
mu
nic
atio
n n
etw
ork
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Wafer Modulewafer beneath heatsink power supplies
48 FPGA communication PCBs
host links
Neuromorphicchip
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 18
Aspects of Modelling Neurobiology : Time and Plasticity
Biology: Analog Neuromorphic Hardware :
Time : 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
Plasticity : 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 modelarea is limited → hybrid model necessary
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Complexity of Synaptic Plasticity is Key to Biological Intelligence
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
Protein complex organization in the
postsynaptic density (PSD)
“Organization and dynamics of PDZ-domain-
related supramodules in the postsynaptic density”
W. Feng and M. Zhang, Nature Reviews NS,
10/2009
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 20
Start with Simple Model : Spike Time Dependent Plasticity
synapse strength decreases
long term depression
synapse strength increases
long term potentiation
presynaptic membrane potential
Dt = tpost – tpre
postsynaptic membrane potentialtime
Dt > 0 |Dt| < tcorrelated Dt < 0
extracellular
stimulation
intracellular
stimulation
Graphs taken from:
Theoretical
Neuroscience by P.
Dayan and L. Abbott,
2001
change in
excitatory
post-
synaptic
potential
long-term
depression
long-term
potentiation
Biological Evidence
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 21
An Example Using Spike-Time-Dependent-Plasticity
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.
Spikey USB basedneuromorphic system
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 22
Using Neuromorphic Hardware : From Networks to Experiments
Mappingimport 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)
RoutingConfiguration/Evaluation(comparing connection matrix)
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 23
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)
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 24
Second Generation Neuromorphic ASIC : HICANN-DLS
analog
network
core
bottom ppu
top ppu
digital
core
logic
fast ADC
vertical
layer1 repeaters
horizontal layer1
repeaters
SERDESchannel 0
output amplifier
main PLL
SERDESchannel 1
SERDESchannel 2
SERDESchannel 3
synthesized RTL
mixed full custom
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
new component : digital plasticity processing units (ppu)
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 25
2nd 65nm Hybrid Plasticity Prototype
plasticity processor
synapse array
neuron circuits
FPGA based controller board
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 26
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
configuration data
SIMD Plasticity Processing Unit
ADC arrayparallel conversion of STDP readout
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 27
Concept of Hybrid Plasticity Operation
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 28
𝜔′− = 𝜔 − 𝑏−𝜔 exp −
∆𝑡
𝑐−
Measurement Results for Multiplicative STDP Rule
𝜔+′ = 𝜔 + 𝑏+ 𝜔max − 𝜔 exp −
Δ𝑡
𝑐+
Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 29
Measurements Demonstrating Possible STDP Rules
Hebbian :Anti-Hebbian :
AsymmetricSensitivity :
Bistablelearning :
• 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 …
Publication currently under review:S. Friedmann, J. Schemmel et.al.:
“Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware System”
The research leading to these results has received funding from theEU FP7 Framework Programme under grant agreement nos.
269921 (BrainScaleS), 243914 (Brain-i-Nets) and 604102 (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!