On-Device Computation for Digital Agriculture
Our lab develops algorithms for resilient data lakes for data generated from Internet-of-Things (IoT) sensors
deployed
in smart and connected farms, that are in various stages of adopting IoT technologies. Here are two vision
papers
demonstrating the emerging directions in this area and our contributions to it.
Relevant projects:
-
Approximate in-sensor analytics: this is for both lightweight workloads such as anomaly detection to detect
the health
of IoT sensors and for heavy-bandwidth workloads, e.g., object detection workloads that require the use of
neural
network variants. The approximation of these algorithms will require techniques such as early exit and low-bit
precision
computations using neural network quantizations.
-
Edge computing: here we are deploying various algorithms on edge servers, which could range from Raspberry Pi
models to
NVIDIA’s Jetson systems.