  Implementation of Neuromorphic Systems using post Complementary
Metal-Oxide-Semiconductor (CMOS) technology based Memristive Crossbar Array
(MCA) has emerged as a promising solution to enable low-power acceleration of
neural networks. However, the recent trend to design Deep Neural Networks
(DNNs) for achieving human-like cognitive abilities poses significant
challenges towards the scalable design of neuromorphic systems (due to the
increase in computation/storage demands). Network pruning [7] is a powerful
technique to remove redundant connections for designing optimally connected
(maximally sparse) DNNs. However, such pruning techniques induce irregular
connections that are incoherent to the crossbar structure. Eventually they
produce DNNs with highly inefficient hardware realizations (in terms of area
and energy). In this work, we propose TraNNsformer - an integrated training
framework that transforms DNNs to enable their efficient realization on
MCA-based systems. TraNNsformer first prunes the connectivity matrix while
forming clusters with the remaining connections. Subsequently, it retrains the
network to fine tune the connections and reinforce the clusters. This is done
iteratively to transform the original connectivity into an optimally pruned and
maximally clustered mapping. Without accuracy loss, TraNNsformer reduces the
area (energy) consumption by 28% - 55% (49% - 67%) with respect to the original
network. Compared to network pruning, TraNNsformer achieves 28% - 49% (15% -
29%) area (energy) savings. Furthermore, TraNNsformer is a technology-aware
framework that allows mapping a given DNN to any MCA size permissible by the
memristive technology for reliable operations.
