  The hardware-efficiency and accuracy of Deep Neural Networks (DNNs)
implemented on In-memory Computing (IMC) architectures primarily depend on the
DNN architecture and the peripheral circuit parameters. It is therefore
essential to holistically co-search the network and peripheral parameters to
achieve optimal performance. To this end, we propose XPert, which co-searches
network architecture in tandem with peripheral parameters such as the type and
precision of analog-to-digital converters, crossbar column sharing and the
layer-specific input precision using an optimization-based design space
exploration. Compared to VGG16 baselines, XPert achieves 10.24x (4.7x) lower
EDAP, 1.72x (1.62x) higher TOPS/W,1.93x (3x) higher TOPS/mm2 at 92.46% (56.7%)
accuracy for CIFAR10 (TinyImagenet) datasets. The code for this paper is
available at https://github.com/Intelligent-Computing-Lab-Yale/XPert.
