  Deep neural networks achieve outstanding results in challenging image
classification tasks. However, the design of network topologies is a complex
task and the research community makes a constant effort in discovering
top-accuracy topologies, either manually or employing expensive architecture
searches. In this work, we propose a unique narrow-space architecture search
that focuses on delivering low-cost and fast executing networks that respect
strict memory and time requirements typical of Internet-of-Things (IoT)
near-sensor computing platforms. Our approach provides solutions with
classification latencies below 10ms running on a $35 device with 1GB RAM and
5.6GFLOPS peak performance. The narrow-space search of floating-point models
improves the accuracy on CIFAR10 of an established IoT model from 70.64% to
74.87% respecting the same memory constraints. We further improve the accuracy
to 82.07% by including 16-bit half types and we obtain the best accuracy of
83.45% by extending the search with model optimized IEEE 754 reduced types. To
the best of our knowledge, we are the first that empirically demonstrate on
over 3000 trained models that running with reduced precision pushes the Pareto
optimal front by a wide margin. Under a given memory constraint, accuracy is
improved by over 7% points for half and over 1% points further for running with
the best model individual format.

Machine Learning;Distributed, Parallel, and Cluster Computing