TraNNsformer: Neural network transformation for memristive crossbar based neuromorphic system design

TL;DR

TraNNsformer is a training framework that optimizes neural networks for memristive crossbar hardware, significantly reducing area and energy consumption while maintaining accuracy, by combining pruning and clustering techniques.

Contribution

It introduces an integrated training method that prunes and clusters DNN connections for efficient memristive crossbar implementation, improving hardware efficiency without accuracy loss.

Findings

Reduces area and energy consumption by up to 55% and 67% respectively.

Achieves 28%-49% area and 15%-29% energy savings over traditional pruning.

Enables mapping DNNs to various memristive crossbar sizes without accuracy loss.

Abstract

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.