  In-memory computing is an emerging computing paradigm that could enable
deeplearning inference at significantly higher energy efficiency and reduced
latency. The essential idea is to map the synaptic weights corresponding to
each layer to one or more computational memory (CM) cores. During inference,
these cores perform the associated matrix-vector multiply operations in place
with O(1) time complexity, thus obviating the need to move the synaptic weights
to an additional processing unit. Moreover, this architecture could enable the
execution of these networks in a highly pipelined fashion. However, a key
challenge is to design an efficient communication fabric for the CM cores.
Here, we present one such communication fabric based on a graph topology that
is well suited for the widely successful convolutional neural networks (CNNs).
We show that this communication fabric facilitates the pipelined execution of
all state of-the-art CNNs by proving the existence of a homomorphism between
one graph representation of these networks and the proposed graph topology. We
then present a quantitative comparison with established communication
topologies and show that our proposed topology achieves the lowest bandwidth
requirements per communication channel. Finally, we present a concrete example
of mapping ResNet-32 onto an array of CM cores.
