Surveillance systems, autonomous vehicles, human monitoring systems, and video retrieval are just few of the many applications in which 3D Convolutional Neural Networks are exploited. However, their extensive use is restricted by their high computational and memory requirements, especially when integrated into systems with limited resources. This study proposes a toolflow that optimises the mapping of 3D CNN models for Human Action Recognition onto FPGA devices, taking into account FPGA resources and off-chip memory characteristics. The proposed system employs Synchronous Dataflow (SDF) graphs to model the designs and introduces transformations to expand and explore the design space, resulting in high-throughput designs. A variety of 3D CNN models were evaluated using the proposed toolflow on multiple FPGA devices, demonstrating its potential to deliver competitive performance compared to earlier hand-tuned and model-specific designs.

Constellation is a distributed network that enables fast, scalable solutions for organizations who need to process and transfer data securely and build interoperability for connected sensors and devices. As computing moves increasingly to the edge, Constellation provides the only scalable, secure solution for a world with more connectivity, more centralization, and more concentrated risk. Constellation Network is a decentralized, open-source, and permissionless programming platform that allows anybody to create apps. Constellation also provides complete control over the tokenomic structure and incentives of apps. Constellation Network, on the other hand, is much more than a decentralized playground and toolbox.