2 research outputs found
Cost-Driven Hardware-Software Co-Optimization of Machine Learning Pipelines
Researchers have long touted a vision of the future enabled by a
proliferation of internet-of-things devices, including smart sensors, homes,
and cities. Increasingly, embedding intelligence in such devices involves the
use of deep neural networks. However, their storage and processing requirements
make them prohibitive for cheap, off-the-shelf platforms. Overcoming those
requirements is necessary for enabling widely-applicable smart devices. While
many ways of making models smaller and more efficient have been developed,
there is a lack of understanding of which ones are best suited for particular
scenarios. More importantly for edge platforms, those choices cannot be
analyzed in isolation from cost and user experience. In this work, we
holistically explore how quantization, model scaling, and multi-modality
interact with system components such as memory, sensors, and processors. We
perform this hardware/software co-design from the cost, latency, and
user-experience perspective, and develop a set of guidelines for optimal system
design and model deployment for the most cost-constrained platforms. We
demonstrate our approach using an end-to-end, on-device, biometric user
authentication system using a $20 ESP-EYE board