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The Artificial Neural Networks (ANNs) like CNN/DNN and LSTM are notbiologically plausible and in spite of their initial success, they cannotattain the cognitive capabilities enabled by the dynamic hierarchicalassociative memory systems of biological brains. The biologically plausiblespiking brain models, for e.g. cortex, basal ganglia and amygdala have agreater potential to achieve biological brain like cognitive capabilities.Bayesian Confidence Propagation Neural Network (BCPNN) is a biologicallyplausible spiking model of cortex. A human scale model of BCPNN in real timerequires 162 TFlops/s, 50 TBs of synaptic weight storage to be accessed with abandwidth of 200 TBs. The spiking bandwidth is relatively modest at 250 GBs/s.A hand optimized implementation of rodent scale BCPNN has been implemented onTesla K80 GPUs require 3 kW, we extrapolate from that a human scale networkwill require 3 MW. These power numbers rule out such implementations for fielddeployment as advanced cognition engines in embedded systems. The keyinnovation that this paper reports is that it is feasible and affordable toimplement real time BCPNN as a custom tiled ASIC in 28 nm technology withcustom 3D DRAM - eBrain II - that consumes 3 kWs for human scale and 12 W forrodent scale cortex model. Such implementations eminently fulfill the demandsfor field deployment.

eBrainII: A 3 kW Realtime Custom 3D DRAM integrated ASIC implementation of a Biologically Plausible Model of a Human Scale Cortex