A current-mode spiking neural classifier with lumped dendritic nonlinearity

Amitava Banerjee, Sougata Kar, Subhrajit Roy, Aritra Bhaduri, Arindam Basu
2015 2015 IEEE International Symposium on Circuits and Systems (ISCAS)  
We present the current mode implementation of a spiking neural classifier with lumped square law dendritic nonlinearity. It has been shown earlier that such a system with binary synapses can be trained with structural plasticity algorithms to achieve comparable classification accuracy with less synaptic resources than conventional algorithms. Hence, in our address event based implementation, we save 2 − 12X memory resources in storing connectivity information. The chip fabricated in 0.35µm CMOS
more » ... has 8 dendrites per cell and uses two opposing cells per class to cancel common mode inputs. Preliminary results show the chip is functional and dissipates 30nW of static power per neuronal cell and 422pJ/spike. DPI Synapse DPI Synapse DPI Synapse χ 2 χ 2 χ 2 Pulse In Address (Dendrite) D0 Dm-1 Dm-1
doi:10.1109/iscas.2015.7168733 dblp:conf/iscas/BanerjeeKRBB15 fatcat:ltvngxttifbcji5ktt5kjbyd6m