A scalable custom simulation machine for the Bayesian Confidence Propagation Neural Network model of the brain
2014 (English)In: 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC), IEEE , 2014, 578-585 p.Conference paper (Refereed)
A multi-chip custom digital super-computer called eBrain for simulating Bayesian Confidence Propagation Neural Network (BCPNN) model of the human brain has been proposed. It uses Hybrid Memory Cube (HMC), the 3D stacked DRAM memories for storing synaptic weights that are integrated with a custom designed logic chip that implements the BCPNN model. In 22nm node, eBrain executes BCPNN in real time with 740 TFlops/s while accessing 30 TBs synaptic weights with a bandwidth of 112 TBs/s while consuming less than 6 kWs power for the typical case. This efficiency is three orders better than general purpose supercomputers in the same technology node.
Place, publisher, year, edition, pages
IEEE , 2014. 578-585 p.
, Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC
3d-stacked drams, Human brain, Hybrid memory, Logic chips, Neural network model, Simulation machine, Synaptic weight, Technology nodes
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-145446DOI: 10.1109/ASPDAC.2014.6742953ISI: 000350791700104ScopusID: 2-s2.0-84897883326ISBN: 978-147992816-3OAI: oai:DiVA.org:kth-145446DiVA: diva2:718809
2014 19th Asia and South Pacific Design Automation Conference, ASP-DAC 2014; Suntec; Singapore; 20 January 2014 through 23 January 2014
QC 201405222014-05-222014-05-212016-04-28Bibliographically approved