  The Artificial Neural Networks (ANNs) like CNN/DNN and LSTM are not
biologically plausible and in spite of 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, for 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 cortex. A human scale model of BCPNN in real time
requires 162 TFlops/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 implemented on
Tesla K80 GPUs require 3 kW, we extrapolate from that a human scale network
will require 3 MW. These power numbers rule out such implementations for field
deployment as advanced 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 ASIC in 28 nm technology with
custom 3D DRAM - eBrain II - that consumes 3 kWs for human scale and 12 W for
rodent scale cortex model. Such implementations eminently fulfill the demands
for field deployment.
