Methods to Evaluate or identify suitable Storage for IoT/AI Boards

"Everyday huge unstructured data gets generated that needs to be processed and used for Artificial intelligence (AI) and IoT (Internet of Things). The intense investment in AI and IoT leading to Rapid innovation in these technologies. These technologies rely on cloud for storing structured and unstructured data, fetching and processing data from cloud is slow due to increased latency and also power consumed will be high, apart from these issues End user is not comfortable in storing data in the cloud due to privacy issues. These issues are pushing AI application to have their intelligence implemented at the edge of network instead of data centers.
Storage is important module at the edge network that can accommodate memory hungry AI processes like Training which uses off-chip memory to keep up with performance improvements. As AI and IoT are becoming famous many professionals and companies use Boards like Raspberry Pi and Arduino which are small inexpensive Boards that allows connecting to various external accessories such as sensors and create Applications.
Famous Boards like Raspberry uses MicorSD card that are of low cost for booting and storing data. This paper explains methods to get benchmarking results and lists important parameters and their values that helps to evaluate which cards needs to be chosen to implement any Applications on Raspberry board. This paper also explain method to emulate one of the AI application that helps to find out life time of MicroSD card for various workloads."

Presented by Sheetal Kulkarni, Senior Staff Engineer & Mythri K, Senior Staff Engineer, Samsung