Transcript Robotic/Human Loops - Computer Science and Engineering

A Novel Multi-GPU
Neural Simulator
Corey Thibeault, Roger Hoang
Frederick C. Harris, Jr.
Brain Computation Laboratory
University of Nevada, Reno
Reno, Nevada
University of Nevada, Reno
UNR Brain Computation Lab
Brain Computation Laboratory
University of Nevada, Reno
Laurence Jayet Fred Harris, Jr Sergiu Dascalu
Director
in funded collaboration with
U de Cergy-Pontoise and CNRS, Paris, France
Brain Mind Institute (Blue Brain Project), EPFL, Lausanne, Switzerland
Mathias Quoy
René Doursat
Henry Markram
Jim King
Scope of Work
Neuroscience
Modeling &
Neural Engineering
Robotic/Human Loops
ONR N000140710018: “Large-Scale Biologically Realistic Models of Cortical & Subcortical Dynamics with Social Robotic Applications”
DURIP N000140510525: “Robotic Platform for Security and Service Applications”
DURIP N000140710704: “Parallel Robotic Brains”
HRL0011-09-C-001: “Project Synapse”
A Little Neuroscience
– Neurons
Excitatory
Interneurons
(inhibitory)
– Columns
High connectivity
within columns.
Less connectivity
across columns
A Little Neuroscience
– Channels
Potassium Family
– M, A, AHP Channels
– Suppressing behavior
on parent cell
– Synapses
Analog converter of
binary spike event.
Contextual filters.
A Little Neuroscience
Brain Slice technology from EPFL
A Little Neuroscience
Voltage Injection & Measurements
A Little Neuroscience
But we need more than 1
– Currently a 12 clamp
A Little Neuroscience
This technology
is used to
measure the
neurons for
reverse
engineering
– Connectivity
– Voltage
Response
Yun Wang, Henry Markram, Philip H. Goodman, Junying Ma, Patricia S. Goldma-Rakic “Novel
Microcircuit Specializations in the Prefrontal Cortex” Nature Neuroscience 2006 Apr;9(4):534-42.
Early Brain Simulation
Artificial Neural Networks (ANNs)
– based on the nonlinear propagation of
average activity (analogous to ring rates)
Some Biologically Realistic Simulators
– Neuron & Genesis
– Very accurate,
– But small models (<10 cells)
NCS History
Version 1:1999
– Matlab
– 160-cell, 2-column architecture
Each cell was modeled as a single integrative
compartment (point neuron) with a spike
mechanism,
– calcium-dependent (AHP) channels, and
– voltage-sensitive A and M (muscarinic) potassium
channels.
M.M. Kellog, H.R. Wills, and P.H. Goodman. “A biologically realistic computer model of neocortical
associative learning for the study of aging and dementia.” J. Investig. Med., 47(2), February 1999
NCS History
Version 1b: 1999
– Direct translation to C from Matlab
Ali Etazadi-Amoli and Keith Weslowski
– 24 times faster.
– tested on mixed excitatory-inhibitory networks
of up to 1,000 cells.
NCS History
Version 2: 1999
– code was then redesigned and rewritten for
distributed processing on an existing 20-cpu
cluster (Pentium II).
– Initial trials of this code were performed on
cortical networks of 2 to 1,000 cells.
NCS History
Version 3: 2001
– completely redesigned using object-oriented design
principles and recoded in C++
– objects, such as cells, compartments, channels, and
the like, model the corresponding cortical entities.
– The cells, in turn, communicate via messages passed
through synapse objects.
– Input parameters allow the user to create many
variations of the basic objects, in order to model
measured or hypothesized biological properties.
E. Courtenay Wilson, Phillip H. Goodman, and Frederick C. Harris, Jr. “Implementation of a
biologically realistic parallel neocortical-neural network simulator” in Proceedings of the 10th SIAM
Conf. on Parallel Process. for Sci. Computing, Portsmouth, Virginia, March 2001.
NCS History
E. Courtenay Wilson, Frederick C. Harris, Jr., and Phillip H. Goodman. “A large-scale biologically
realistic cortical simulator” in Proceedings of SC 2001, Denver, Colorado, November 2001
Hardware
Several grants from DoD for
hardware
– 2001 – 30 dual PIII (2GB/core)
– 2002 – 34 dual PIV
(2GB/core), Myrinet connection
– 2004 – 40 dual opterons (2
GB/core)
– 2007 – 4 16core opteron boxes
(128GB/box) and 3 24TB disk
arrays, Infiniband connection
Web Interface
Goals:
– Allow rapid creation
of brain models
– Allow remote
collaboration
Kishor K. Waikul, Lianjun Jiang, E. Courtenay Wilson, Frederick C. Harris, Jr., and Philip H. Goodman,
“Design and Implementation of a Web Portal for a NeoCortical Simulator,” in Proceedings of the 17th
International Conference on Computers and Their Applications (CATA 2002) pp. 349-353, April 4-6,
2002, San Francisco, CA
Code Optimization & Revisions
Rewrote the input parser
Worked on code base
– sevenfold sequential speedup over the
version 3 code
– added new features while shrinking our code
base by more than 25%.
Added More Biological Parameters.
35,000 cells and approximately 6.1 million
synapses using 72% of the available 4GB
of memory per node.
Code Optimization
James Frye, James G. King, Christine J. Wilson, and Frederick C. Harris, Jr. “QQ: Nanoscale timing
and profiling” In Proceedings of PMEO-PDS, Denver, CO, April 3-8 2005.
Modeling &
Neural Engineering
Ripplinger MC, Wilson CJ, King JG, Frye J, Drewes R, Harris FC, and Goodman PH, “Computational
Model of Interacting Brain Networks,” Journal of Investigative Medicine, Vol. 52, No. 1, Jan 2004, pp.
S155.
Modeling &
Neural Engineering
Modeling &
Neural Engineering
(bAC)
KAHP
Modeling &
Neural Engineering
Romain Brette, Michelle Rudolph, Ted Carnevale, Michael Hines, David Beeman, James M. Bower,
Markus Diesmann, Abigail Morrison, Philip H. Goodman, Frederick C. Harris, Jr., Milind Zirpe, Thomas
Natschlager, Dejan Pecevski, Bard Ermentrout, Mikael Djurfeldt, Anders Lansner, Olivier Rochel,
Thierry Vieville, Eilif Muller, Andrew P. Davison, Sami El Boustani and Alain Destexhe, “Simulation of
networks of spiking neurons: A review of tools and strategies,” Journal of Computational Neuroscience
Vol. 23, December, 2007, pgs 349-398.
Modeling &
Neural Engineering
extensive domain
of self-sustained
asynchronous
irregular firing
R
N
Modeling &
Neural Engineering
RAIN Firing Characteristics
Robotic/Human Loops
Juan C. Macera, Philip H Goodman, Frederick C. Harris, Jr., Rich Drews, and James B. Maciokas
“Remote-Neocortex Control of Robotic Search and Threat Identification,” Robotics and
Autonomous Systems, Vol. 46, No. 2, February 2004, pp 97-110.`
Robotic/Human Loops
Qunming Peng “Brainstem: A NeoCortical Simulator Interface for Robotic Studies”, MS Thesis
December 2006, University of Nevada,Reno
Robotic/Human Loops
Coarse Gabor Filters
transduced into a
raster of spikes
VC
30
Robotic/Human Loops
Engaging & Rewarding
Evocative
31
Robotic/Human Loops
Virtual Robots
Philip H. Goodman, Sermsak Buntha, Quan Zou, Sergiu M. Dascalu, :Virtual neurorobotics (VNR) to
accelerate the development of plausible neuromorphic brain architectures” Frontiers in Neurorobotics
1(1) pp. 1-7 November 2007.
Other Preliminary Work
General Movement Disorders
– Parkinsons
– Autism
Goal: model properly
– That way we can study changes
Blue Brain
EPFL: IBM BlueGene, 8096-CPU cluster,
22 Trillion Flops
Objectives:
– Understand scientific basis for superiority of
human intelligence over current machine
learning and AI
– Create neurally-based cognitively intelligent
systems
– Develop neuromorphic robots which interact
with humans
– Complement Neuroscience wet lab and
cognitive research
Impacts:
– Multi-compartment and million-cell brain
models
– New theory of “mesocircuit” to link biology and
behavior
– Robotic prototype advanced from CANINE to
HUMANOID robot
– Enhanced new field of “virtual neurorobotics”
(VNR)
Present Scope of Work
Neuroscience
Modeling &
Neural Engineering
Robotic/Human Loops
Goals
A Novel Multi-GPU
Neural Simulator
The proof-of-concept simulation code
described here is presented as an
illustration of both the design's scalability
and performance potential once integrated
to the existing environment.
Written in CUDA
– With hooks for MPI
A Novel Multi-GPU
Neural Simulator
Supports the same input file as NCS
– We are adding support for NeuroML
Once input has been read, Initialization
begins.
Initialization Overview
The neurons are sorted based on the
number of synaptic connections.
These are then distributed to the
respective GPUs in a round-robin fashion.
Then simulation begins
Initialization Overview
Local Data structures are then created.
Simulation
Now that we are finished with setup we go
on to the simulation:
Simulation
The simulation begins by updating
(numerically integrating) the neurons.
Simulation
The cell firings are exchanged and we
update the cell computations
Testing
Some basic benchmarks were run to
illustrate the scalability and functionality of
the design.
The test network was based on the
polychronization models from Izhikevich et
al. [10] and Szat et al. [11].
Testing
This network utilizes a ratio of four
excitatory neurons to 1 inhibitory neuron.
– M/N is the probability of connections
– M is the number of connections per Neuron. N
is the number of Neurons.
Results
Results
Small amount of data to transfer
100,000 neurons with 50 connections per
neuron can run at 1.2 times faster than
real time.
Conclusions/Future Work
This was a worst case scenario.
– And it is better than real time.
We have already moved to a cluster of
boxes of GPUs [and have a proposal out
for money to purchase a better/larger GPU
cluster]
A Novel Multi-GPU
Neural Simulator
Corey Thibeault, Roger Hoang
Frederick C. Harris, Jr.
Brain Computation Laboratory
University of Nevada, Reno