A Scalable Custom Simulation Machine for the Bayesian Confidence Propagation Neural Network model of the Brain
CBN (Computational Biology and Neurocomputing) seminars
Friday 13 December 2013
to 11:00 at
Anders Lansner and Ahmed Hemani (Dept. of Computational Biology and Dept. of Electronic Systems, KTH)
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.