  Recent advancements in machine learning algorithms, especially the
development of Deep Neural Networks (DNNs) have transformed the landscape of
Artificial Intelligence (AI). With every passing day, deep learning based
methods are applied to solve new problems with exceptional results. The portal
to the real world is the edge. The true impact of AI can only be fully realized
if we can have AI agents continuously interacting with the real world and
solving everyday problems. Unfortunately, high compute and memory requirements
of DNNs acts a huge barrier towards this vision. Today we circumvent this
problem by deploying special purpose inference hardware on the edge while
procuring trained models from the cloud. This approach, however, relies on
constant interaction with the cloud for transmitting all the data, training on
massive GPU clusters, and downloading updated models. This is challenging for
bandwidth, privacy, and constant connectivity concerns that autonomous agents
may exhibit. In this paper we evaluate techniques for enabling adaptive
intelligence on edge devices with zero interaction with any high-end
cloud/server. We build a prototype distributed system of Raspberry Pis
communicating via WiFi running NeuroEvolutionary (NE) learning and inference.
We evaluate the performance of such a collaborative system and detail the
compute/communication characteristics of different arrangements of the system
that trade-off parallelism versus communication. Using insights from our
analysis, we also propose algorithmic modifications to reduce communication by
up to 3.6x during the learning phase to enhance scalability even further and
match performance of higher end computing devices at scale. We believe that
these insights will enable algorithm-hardware co-design efforts for enabling
continuous learning on the edge.
