  Deep neural network (DNN) accelerators with improved energy and delay are
desirable for meeting the requirements of hardware targeted for IoT and edge
computing systems. Convolutional neural networks (CoNNs) belong to one of the
most popular types of DNN architectures. This paper presents the design and
evaluation of an accelerator for CoNNs. The system-level architecture is based
on mixed-signal, cellular neural networks (CeNNs). Specifically, we present (i)
the implementation of different layers, including convolution, ReLU, and
pooling, in a CoNN using CeNN, (ii) modified CoNN structures with CeNN-friendly
layers to reduce computational overheads typically associated with a CoNN,
(iii) a mixed-signal CeNN architecture that performs CoNN computations in the
analog and mixed signal domain, and (iv) design space exploration that
identifies what CeNN-based algorithm and architectural features fare best
compared to existing algorithms and architectures when evaluated over common
datasets -- MNIST and CIFAR-10. Notably, the proposed approach can lead to
8.7$\times$ improvements in energy-delay product (EDP) per digit classification
for the MNIST dataset at iso-accuracy when compared with the state-of-the-art
DNN engine, while our approach could offer 4.3$\times$ improvements in EDP when
compared to other network implementations for the CIFAR-10 dataset.
