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1.	Stathis, Dimitrios (författare) Going Beyond the eBrainII: Exploiting temporal locality and lazy evaluation of post-synaptic spikes 2020 Rapport (övrigt vetenskapligt/konstnärligt)abstract Bayesian Confidence Propagation Neural Network is a biologically plausible spiking model of the cortex. The human cortex is comprised of 100 billion neurons, a human-scale model of BCPNN in real-time requires 162 TFLOPS, 50 TB of synaptic weight storage to be accessed with a bandwidth of 200 TB. The spiking bandwidth is relatively modest at 200 GB/s. In this report, we present the initial results of an ASIC implementation of the BCPNN model. This work is in progress, and here we showcase how we can explore the BCPNN’s inherit data locality. The base-line implementation, called eBrainII, consumes 3 kW for real-time, human-scale BCPNN model. We improve in the base-line by adopting a lazy column update model that eliminates the expensive column access to DRAM and reduces the power consumption to 1.7 kW. We further exploit the significant temporal locality of input spikes to reduce the DRAM power to 17% and the total power consumption to 700 Watts. The implementation is highly regular and tiled, requiring modest engineering effort.

Stathis, Dimitrios (författare)
Going Beyond the eBrainII: Exploiting temporal locality and lazy evaluation of post-synaptic spikes
2020
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Bayesian Confidence Propagation Neural Network is a biologically plausible spiking model of the cortex. The human cortex is comprised of 100 billion neurons, a human-scale model of BCPNN in real-time requires 162 TFLOPS, 50 TB of synaptic weight storage to be accessed with a bandwidth of 200 TB. The spiking bandwidth is relatively modest at 200 GB/s. In this report, we present the initial results of an ASIC implementation of the BCPNN model. This work is in progress, and here we showcase how we can explore the BCPNN’s inherit data locality. The base-line implementation, called eBrainII, consumes 3 kW for real-time, human-scale BCPNN model. We improve in the base-line by adopting a lazy column update model that eliminates the expensive column access to DRAM and reduces the power consumption to 1.7 kW. We further exploit the significant temporal locality of input spikes to reduce the DRAM power to 17% and the total power consumption to 700 Watts. The implementation is highly regular and tiled, requiring modest engineering effort.

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