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FROM BRAIN RESEARCH TO FUTURE TECHNOLOGIES
Dirk Pleiter | Post-H2020 Vision for HPC Workshop, Frankfurt | 24.06.2018
Science Challenge and Benefits
Understanding the human brain• Understand the organisation of the brain• Understand brain functioning and dysfunction
Social and economic impact• Enable treatment of brain diseases• Enable new approaches to artificial intelligence
Complexity of the brain• About 90 billion nerve cells, each with 10,000 connections• Multi-scale challenge
Need for interdisciplinary approaches• Neuroscience, medicine, physics etc.• Computer science and engineering, mathematics etc.
224.06.2018
Whole braincm scale
Neuronμm to mm scale
Synapsenm to μm scale
The Human Brain Project
Flagship project funded by the EC• Science-driven, seeded from Future and Emerging Technologies, extending beyond ICT• Started in 2013 as a 10 years efforts• More than 110 partners in 19 countries
Objectives of the HBP• Realise an European scientific Research Infrastructure for brain research/brain-inspired sciences• Gather, organise and disseminate data describing the brain and its diseases• Simulate the brain• Build multi-scale scaffold theory and models for the brain• Develop brain-inspired computing, data analytics and robotics• Ensure that the HBP's work is undertaken responsibly and that it benefits society
324.06.2018
A Case for Co-Design
Need for of a cutting-edge ICT-based scientific research infrastructure• Simulate the brain• Gather, organise and disseminate data
Brain research seed future and emerging technology• Develop brain-inspired computing, data analytics and robotics
424.06.2018
ENABLING BRAIN RESEARCH THROUGH NEW ARCHITECTURES AND TECHNOLOGIES
High-Resolution Brain Models from Histological Data
Research goal• Accurate, highly detailed computer model of the
human brain based on histological input
Approach• Create high-resolution 2-dimensional brain
section images (1μm resolution)• Re-construct 3-dimensional models from these
images• Reconstruct nerve fibers in histological brain
sections• Automatised analysis of high-resolution images
624.06.2018
[K. Amunts et al., Science 2013]
[M. Axer et al.]
Data Analytics on Extreme-Scale Data Volumes
Data challenge• Data acquisition using 8-9 microscopes at ~15 MByte/s → ~10 TByte/day• Archival data increase 1-2 PByte per year
Data analytics challenge• Deep learning methods for automatised cell segmentation• Massively parallel feature extraction (e.g. cell count)• Image registration
Data access speed challengee• Example of multi-GPU training requirements
(using P100 GPUs)
724.06.2018
DataAcquisition
Site local storage
PermanentHPC storage
Fast HPC storage
HPC/HPDA systemΔ t IO < ϵ Δ t comp → BIO >
NGPUϵ 0.3GByte / s
Need for improved data transport capabilities and support for high performance data analytics workflows
[K. Amunts, T. Dickscheid et al.]
Dynamical Numerical Brain Models
Research goal• Create biologically realistic models of the brain
– Realistic size of models– Realistic level of detail
Multi-scale challenge• Multi-scale simulations based on co-simulation of different models
Supercomputer requirements• Maximize memory footprint
– Application today is memory capacity limited• Optimize processing performance → memory performance
– Keep ratio simulation versus simulated time small
Allow for interactive steering of the applications• Requires interactive visualisation as well as low-latency control loop
824.06.2018
Need for scalable compute resources (efficiently exploitable for applications)
Brain Simulation Workflows
924.06.2018
HPC system
PermanentHPC storage
Fast HPC storage
Interactive viz
Jupyter notebook
Knowledge Graph
Need for interactive compute services integrated in HPC centres
Coupling HPC Systems and External Facilities
Closed-loop brain and robot model based experiments• Brain simulation coupled to robot simulation engines
1024.06.2018
Need for bridges to external services
Integration and Creation of Knowledge
Enable ICT platforms integrating different discovery steps• Experiment• Model building and simulation• Data integration and strategic data collection
Creation of knowledge spaces• Creation of data sets curated following agreed procedures• Improve on tapping all possible data sources (including
unstructured data sources• Creation of knowledge spaces
Federation of services• No centralisation → be open and allow for extensions• Allow for integration of very heterogeneous compute
services or data sources
1124.06.2018
Need for integration of various compute and data services
Need for infrastructure federation
Knowledge and Theory
Experiment
Data
Simulation
New experiments New models
Other data sources
Need for technologies tapping all relevant data sources
Medical Informatics: Coping with Sensitive Data
Objective• Enable access to distributed harmonised pan-omics patient data for clinical and fundamental brain research
– E.g., electronic health records (EHR), picture archiving and communication systems (PACS)
Challenge• Use of personal data
Strategies• Securing data repositories for personal data• Introduce de-identification mechanisms and services
– Pseudonomisation– Anonymisation
1224.06.2018
Need for technical means supporting- Securing data storage and access- Secure provisioning of de-identification services
Service-Oriented Provisioning of HPC/HPDA Resources
Provisioning of broad range of integrated infrastructure services• Different types of compute services including
– Scalable compute services– Interactive compute services– VM services
• Different types of data stores
Develop and deploy services that facilitate federation• Federation of AAI, monitoring etc.
Encourage communities to build community specific platforms• Push for further separation of concerns• Close engagement with communities
1324.06.2018
Need for support of new HPC use models including- Flexible provisioning of hardware resources- Supporting federation of infrastructure- Support for new compute devices (NC, QC)
FROM BRAIN RESEARCH TO NEW ARCHITECTURES AND TECHNOLOGIES
Learning from the Brain: Extremely High Connectivity
Large Rent’s exponent• Rent’s rule: Scaling of connections as function of
logic element
• Typical value for micro-processors: p ≈ 0.3 … 0.8• Smaller values of p outside of micro-procesor• Estimated value for mammalian brains: p ≈ 0.8
1524.06.2018
Brain as inspiration for improving on new approaches to improve data transport capabilities and space filling computers
C=k N p
[P. Ruch et al., 2011]
[Landman and Russo, 1971;M.Y. Lanzerotti et al., 2005; D.S. Bassett et al., 2010 ]
Learning from the Brain: Extremely Small Power Density
Smaller frequency and power density• Brain based on massively-parallel distributed
architecture• Micro-processors based on von-Neumann
architecture are much sequential and centralised
Analogue logics• Analogue logics can be much faster for certain
operations, e.g. time integration for neural cell membrane
1624.06.2018
Brain as inspiration for neuromorphic processing architectures and massively-parallel digital-analogue circuitries for compute
[Merolla et al., Science, 2014]
HBP’s Neuromorphic Systems
SpiNNaker @ Manchester
1724.06.2018
BrainScales @ Heidelberg
[K. Meier et al.][S. Furber et al.]
New Approaches to Artificial Intelligence
Today: Deep learning based on algorithms and implementations that are different from those used by our brains• Currently used ANN+algorithms quite successful but very compute intensive
Principles of learning in the brain seem to be different• Neural circuits are highly recurrent networks consisting of different types of neurons and synapses with diverse
dynamic properties• Networks of neurons in the brain provide stable computational function in spite of ongoing rewiring and network
perturbations
Research starts leading to new learning algorithms• Algorithms particular suitable for neuromorphic hardware
1824.06.2018
[Nature Editorial, 2/2018]
Brain as inspiration for new approaches to artificial intelligence and possibly new semiconductor innovations
[W. Maass, 2017, 10.1016/j.cobeha.2016.06.003]
SUMMARY AND CONCLUSIONS
Summary and Conclusions
Science drivers are key for defining future HPC system/infrastructure architecture and technology roadmaps• Brain research is only one research area• Others include, e.g., earth system modelling (including weather)
Brain research as driver for architectures and technologies• Exascale computing and beyond• Creation of federated, ICT-based research infrastructures integrating various compute and storage
resources connected to various data sources and coupled to external experiments• Convergence of HPC and “big data” and cloud
Brain research as enabler for new technologies• Neuromorphic computing• New approaches to artificial intelligence
2024.06.2018
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