Engineering Next-generation Self-healing And Self-

optimizing Neural Network Based Medical PlatformsZhanpeng Jin Allen C. Cheng

zhj6@pitt.edu acc33@pitt.edu

ASPLOS 2010, The Wild and Crazy Session VIII

Artificial Neural Network

(Source: "Anatomy and Physiology" by the US National Cancer Institute's Surveillance, Epidemiology and End Results (SEER)

Program.)

(Source: CapitalISM BI)

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

Bio-inspired Autonomously Reconfigurable Mechanism

AutonomousReconfigurability

Topology Adaptation

Redundancy

(Picture Source: “Brain Injury: Recovery” in Psychology Wiki)

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

Virtual-physical Neuron Mapping

Neural Topology Physical NeuronsVirtual-to-Physical

(V2P)Neuron Mapping

Topology Adaptation

(Coarse-grained)

Connectionism Evolvement

(Fine-grained)Autonomous

ANN+ =

Mask-based Topology Adaptation

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.

Thanks for Listening

Questions?

(This work is supported by NSF No. 0832990)