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eBrainII : a 3 kW Realtime Custom 3D DRAM Integrated ASIC Implementation of a Biologically Plausible Model of a Human Scale Cortex

Stathis, Dimitrios (author)
KTH,Elektronik och inbyggda system
Sudarshan, Chirag (author)
University of Kaiserslautern, Kaiserslautern, Germany
Yang, Yu (author)
KTH,Elektronik och inbyggda system
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Jung, Mathias (author)
Fraunhofer IESE, Kaiserslautern, Germany
Weis, Christian (author)
University of Kaiserslautern, Kaiserslautern, Germany
Hemani, Ahmed, 1961- (author)
KTH,Elektronik och inbyggda system
Lansner, Anders, Professor (author)
Stockholms universitet,KTH,Beräkningsvetenskap och beräkningsteknik (CST),Matematiska institutionen,KTH Royal Institute of Technology, Sweden
Wehn, Norbert (author)
University of Kaiserslautern, Kaiserslautern, Germany
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 (creator_code:org_t)
2020-07-07
2020
English.
In: Journal of Signal Processing Systems. - : Springer. - 1939-8018 .- 1939-8115. ; 92:11, s. 1323-1343
  • Journal article (peer-reviewed)
Abstract Subject headings
Close  
  • The Artificial Neural Networks (ANNs), like CNN/DNN and LSTM, are not biologically plausible. Despite their initial success, they cannot attain the cognitive capabilities enabled by the dynamic hierarchical associative memory systems of biological brains. The biologically plausible spiking brain models, e.g., cortex, basal ganglia, and amygdala, have a greater potential to achieve biological brain like cognitive capabilities. Bayesian Confidence Propagation Neural Network (BCPNN) is a biologically plausible spiking model of the cortex. A human-scale model of BCPNN in real-time requires 162 TFlop/s, 50 TBs of synaptic weight storage to be accessed with a bandwidth of 200 TBs. The spiking bandwidth is relatively modest at 250 GBs/s. A hand-optimized implementation of rodent scale BCPNN has been done on Tesla K80 GPUs require 3 kWs, we extrapolate from that a human scale network will require 3 MWs. These power numbers rule out such implementations for field deployment as cognition engines in embedded systems. The key innovation that this paper reports is that it is feasible and affordable to implement real-time BCPNN as a custom tiled application-specific integrated circuit (ASIC) in 28 nm technology with custom 3D DRAM - eBrainII - that consumes 3 kW for human scale and 12 watts for rodent scale. Such implementations eminently fulfill the demands for field deployment.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Keyword

3D DRAM
ASIC
BCPNN
Custom 3D DRAM
Machine learning
Neural Network
Neural network architecture
Neuromorphic computing
Application specific integrated circuits
Associative processing
Backpropagation
Bandwidth
Brain models
Cognitive systems
Dynamic random access storage
Embedded systems
Hierarchical systems
Program processors
Scales (weighing instruments)
Associative memory system
Cognitive capability
Field deployment
Optimized implementation
Plausible model
Scale modeling
Spiking model
Synaptic weight
Long short-term memory

Publication and Content Type

ref (subject category)
art (subject category)

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