  Deep neural networks (DNNs) have made breakthroughs in various fields
including image recognition and language processing. DNNs execute hundreds of
millions of multiply-and-accumulate (MAC) operations. To efficiently accelerate
such computations, analog in-memory-computing platforms have emerged leveraging
emerging devices such as resistive RAM (RRAM). However, such accelerators face
the hurdle of being required to have sufficient on-chip crossbars to hold all
the weights of a DNN. Otherwise, RRAM cells in the crossbars need to be
reprogramed to process further layers, which causes huge time/energy overhead
due to the extremely slow writing and verification of the RRAM cells. As a
result, it is still not possible to deploy such accelerators to process
large-scale DNNs in industry. To address this problem, we propose the BasisN
framework to accelerate DNNs on any number of available crossbars without
reprogramming. BasisN introduces a novel representation of the kernels in DNN
layers as combinations of global basis vectors shared between all layers with
quantized coefficients. These basis vectors are written to crossbars only once
and used for the computations of all layers with marginal hardware
modification. BasisN also provides a novel training approach to enhance
computation parallelization with the global basis vectors and optimize the
coefficients to construct the kernels. Experimental results demonstrate that
cycles per inference and energy-delay product were reduced to below 1% compared
with applying reprogramming on crossbars in processing large-scale DNNs such as
DenseNet and ResNet on ImageNet and CIFAR100 datasets, while the training and
hardware costs are negligible.
