  In-Memory Computing (IMC) hardware using Memristive Crossbar Arrays (MCAs)
are gaining popularity to accelerate Deep Neural Networks (DNNs) since it
alleviates the "memory wall" problem associated with von-Neumann architecture.
The hardware efficiency (energy, latency and area) as well as application
accuracy (considering device and circuit non-idealities) of DNNs mapped to such
hardware are co-dependent on network parameters, such as kernel size, depth
etc. and hardware architecture parameters such as crossbar size. However,
co-optimization of both network and hardware parameters presents a challenging
search space comprising of different kernel sizes mapped to varying crossbar
sizes. To that effect, we propose NAX -- an efficient neural architecture
search engine that co-designs neural network and IMC based hardware
architecture. NAX explores the aforementioned search space to determine kernel
and corresponding crossbar sizes for each DNN layer to achieve optimal
tradeoffs between hardware efficiency and application accuracy. Our results
from NAX show that the networks have heterogeneous crossbar sizes across
different network layers, and achieves optimal hardware efficiency and accuracy
considering the non-idealities in crossbars. On CIFAR-10 and Tiny ImageNet, our
models achieve 0.8%, 0.2% higher accuracy, and 17%, 4% lower EDAP
(energy-delay-area product) compared to a baseline ResNet-20 and ResNet-18
models, respectively.
