Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-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 PhysicsJohannes SchemmelRuprecht-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 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

More Neuronal Diversity : Adaptive-Exponential Integrate-and-Fire

• 180 nm CMOS• calibration parameters stored on

analog floating gates

Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 5

Example Membrane Voltage Traces of HICANN V4

# of Synaptic inputs : 12 4

Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 6

Six Groups of Neurons Firing in a Chain

Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 7

Wafer Modulewafer beneath heatsink power supplies

48 FPGA communication PCBs

host links

network chip

Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 8

More Neuronal Diversity : Adaptive-Exponential Integrate-and-Fire

• 180 nm CMOS• calibration parameters stored on

analog floating gates

Machine Room

Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-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()[...]

PyNN script(reordered connection matrix)

RoutingConfiguration/Evaluation(comparing connection matrix)

Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 10

Hybrid Plasticity

Problem : millions of parameters• network topology• neuron sizes and AdEx-parameters• synaptic strengthsCurrent 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 11

Second Generation Neuromorphic ASIC : HICANN-DLS

analognetwork

core

bottom ppu

top ppu

digitalcorelogic

fast ADC

verticallayer1

repeaters

horizontal layer1

repeaters

SERDESchannel 0

output amplifier

main PLL

SERDESchannel 1

SERDESchannel 2

SERDESchannel 3

synthesized RTLmixed full customanalog full custom

analog outputs

TX data

TX clk

RX clk

RX data

extclkJTAG and reset

TX dat

L1 top

L1 right

L1 left

L1 bot

synapse tl, tr, bl, br

Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-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 configuration data

SIMD Plasticity Processing Unit

ADC arrayparallel conversion of STDP readout

Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 13

NM-PM2 Prototype

plasticity processor

synapse array

neuron circuits

FPGA based controller board

Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-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 PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 15

𝜔 ′−=𝜔−𝑏−𝜔 exp(− ∆ 𝑡

𝑐−)

Measurement Results for Multiplicative STDP Rule

𝜔+¿ ′=𝜔+𝑏+¿ (𝜔max−𝜔 ) exp¿ ¿¿ ¿

Kirchhoff Institute for PhysicsJohannes SchemmelRuprecht-Karls-Universität Heidelberg 16

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 …

The research leading to these results has received funding from the EU FP7 Programme under grant

agreement nos. 269921 (BrainScaleS) 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!