Abstract

By using Reactive, Associative, Exact or Fuzzy Matching, Massively Parallel, Trainable, Fixed Latencies, Low-Power, NeuroMem e is a unique architecture of Neuromorphic Memories chips which react to digital stimuli and can learn and recognize in real time. With their RBF classifier neurons could detect novelty, cope with non-linearity and ill-defined problems, report cases of unknown and cases of uncertainty. Learning how neuromorphic chips are opening new frontiers for smart sensors and cognitive computing applications. It can solve pattern recognition problems from text and data analytics, vision, audition, and multi-sensory fusion with orders of magnitude less energy and complexity than modern microprocessors. Learning how to use NeuroMem in all the following applications – Number analytics: stock trading, financials, meteorology, and physics – Text analytics: sentiments analytics, marketing, economics, and bioinformatics – Signal analytics: industrial components, wearables, toys, and sports appliances – Video analytics: face, gesture, surveillance, automotive and building automation – Network security: secure uplink/downlink, denial of service attack – Many more applications for real-time and low power pattern recognizability – Prepare students for making their own POC (prove of concepts)

Speaker Profile

Dr Pierre Brunswick has deep knowledge of IT hardware including chip technologies, AI, boards design, storage, network, servers, mainframes, HPC, cyber security and full processes up to smart cities. Highly skilled teacher and educator especially on Al neuromorphic technology on all possible applications for Al. Developed significant local government relationships (including technology transfers, big infrastructure projects and top technology specific programs), working with financial institutions & elaborated many educational programs, including incubator for start-ups. With a total of 40 years of international experience in business Prof. Dr. Pierre Brunswick received many honored tittle including: Elected Knight from the Russian Academy of Sciences in 2005.