Deep Learning Neural Network Acceleration at the Edge
The dramatically growing amount of data captured by sensors and the ever more stringent requirements for latency and real time constraints are paving the way for edge computing, and this implies that …
| Talk Title | Deep Learning Neural Network Acceleration at the Edge |
| Speakers | Andrea Gallo (VP of Membership Development, Linaro) |
| Conference | Open Source Summit North America |
| Conf Tag | |
| Location | Vancouver, BC, Canada |
| Date | Aug 27-31, 2018 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
The dramatically growing amount of data captured by sensors and the ever more stringent requirements for latency and real time constraints are paving the way for edge computing, and this implies that not only big data analytics but also AI inference shall be executed at the edge. The multiple options for neural network acceleration in recent Arm-based platforms provides an unprecedented opportunity for new intelligent devices with AI inference. It also brings the risk of fragmentation and duplication of efforts when multiple frameworks shall support multiple accelerators. Andrea Gallo, Linaro VP of Segment Groups, will summarise the existing NN frameworks, model description formats, accelerator solutions, low cost development boards and will describe the efforts underway to identify the best technologies to improve the consolidation and enable the competitive innovative advantage from all vendors.
