Constrained deep neural network architecture search for IoT devices accounting hardware calibration

Understand

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.