Zhanpeng Jin Allen C. Cheng zhj6@pitt acc33@pitt
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Transcript of Zhanpeng Jin Allen C. Cheng zhj6@pitt acc33@pitt
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Engineering Next-generation Self-healing And Self-
optimizing Neural Network Based Medical PlatformsZhanpeng Jin Allen C. Cheng
[email protected] [email protected]
ASPLOS 2010, The Wild and Crazy Session VIII
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Artificial Neural Network
(Source: "Anatomy and Physiology" by the US National Cancer Institute's Surveillance, Epidemiology and End Results (SEER)
Program.)
(Source: CapitalISM BI)
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Emerging Neural Hardware
Neural chip (384 neurons + 100,000 synapses) @ FACET Project
Microchip of a neural network @ RIEC, Tohoku University
The China-Brain Project (10,000 – 15,000 neural nets) @ Hugo de Garis
3-D neural chip for better visualization @ CalTech
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Bio-inspired Autonomously Reconfigurable Mechanism
AutonomousReconfigurability
Topology Adaptation
Redundancy
(Picture Source: “Brain Injury: Recovery” in Psychology Wiki)
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Autonomously Reconfigurable ANN
Autonomously Reconfigurable ANN (ARANN) Based Medical
Processing Platforms
Error Detectio
n
µControllerRun-Time Reconfiguration
Controller
Mask-Based Topology Adaption
Coarse-/Fine-Grained Hybrid Reconfiguration
New topology
Flash MemoryCoarse-grained Reconfiguration
Bitstream Database
Curre
nt A
NN
Confi
gura
tion
Fine-grained Reconfiguration
Bitstream
New Configuration Select/Merge Bitstreams
Coarse-grained Reconfiguration
Bitstream
Error Scale and Location
Outputs
Data Traffic
Request
Request
Inputs Sensors/DatabaseDiagnosis
/Controller
s
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Virtual-physical Neuron Mapping
Neural Topology Physical NeuronsVirtual-to-Physical
(V2P)Neuron Mapping
Topology Adaptation
(Coarse-grained)
Connectionism Evolvement
(Fine-grained)Autonomous
ANN+ =
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Mask-based Topology Adaptation
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WACI Conclusion Systems are increasingly vulnerable to unexpected faults
and defects, especially for emerging biomedical systems.
Non-invasive autonomous reconfigurability is promising, particularly for ANN-based biomedical platforms.
Autonomously adapting ANN’s behaviors and structures, both algorithmically and microarchitecturally. Neuron Virtualization helps to decouple the fault scale and
reduce the reconfiguration latency (idle time). Mask-based Topology Adaptation can achieve significant
reduction of design complexity and spatial overhead.
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Thanks for Listening
Questions?
(This work is supported by NSF No. 0832990)