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.

像CNN / DNN和LSTM这样的人工神经网络（ANN）在生物学上是不合理的。尽管取得了最初的成功，但他们无法获得生物大脑的动态分层联想记忆系统所实现的认知能力。生物学上可行的尖峰大脑模型，例如皮质，基底神经节和杏仁核，具有更大的潜力，可以实现类似于认知能力的生物学大脑。贝叶斯置信度传播神经网络（BCPNN）是皮层的生物学上可行的尖峰模型。BCPNN的人类规模实时模型需要162 TFlop / s，50 TB的突触重量存储和200 TB的带宽访问。峰值带宽相对适中，为250 GBs / s。已在需要3 kWs的Tesla K80 GPU上进行了手动优化的啮齿动物规模BCPNN实施，我们推断，一个人类规模的网络将需要3兆瓦。这些功率数字排除了诸如嵌入式系统中的认知引擎之类的用于现场部署的实现方式。

本文报告的主要创新之处在于，采用定制的3D DRAM-eBrainII-以3 kW的人工规模将实时BCPNN实施为采用28 nm技术的定制平铺专用集成电路（ASIC），这是可行且负担得起的啮齿动物秤的功率为12瓦。这样的实现非常满足现场部署的需求。