Dr. Manan Suri, professor at the Indian Institute of Technology, Delhi and his team from the Department of Electrical Engineering have conceptualized and qualified a system combining two NeuroMem neural networks to accurately authenticate persons based on their voice and face. The hardware platform includes a NeuroMem CM1K chip so its 1024 neurons can perform the learning and classification of patterns in real-time and near sensor.
General Vision was invited to participate to a webcast organized by QuickLogic on a new AI Ecosystem targeting end points. Indeed, with the advent of Industry 4.0 and the proliferation of IoT, there is a need to make devices that process sensor data and make decisions locally. Traditional machine learning algorithms are too memory and power hungry to migrate AI to endpoints with constrained battery life. This calls for NeuroMem neurons with zero latency, low power, low cost and easy to train.
Among the participants were nepes, manufacturer of the NeuroMem NM500 chip, and SensiML, developer of smart sensor algorithms. During his speech, Guy Paillet, CEO of General Vision, emphasized the synergy between the NeuroMem trainable neurons and eFPGAs to design ultra low-power and adaptive search engines. QuickLogic introduced their Quick AI HDK platform integrating their EOS SOC and a network of 2 NM500 chips.
Oceanit teamed up with Kauai Coffee Company and Kamehameha Schools to develop an innovation that can ‘see’ the ripeness of coffee cherries utilizing a NeuroMem neural network and win Hawaii’s first annual AGathon. By using a portable prediction system to determine ripeness, Kauai Coffee’s harvest values could be improved by more than a quarter million dollars per harvest. Read the complete report.
The Oceanit team developed a system around the Raspberry PI equipped with RaspiCam vision module and a shield board populated with two NeuroMem NM500 chips.
Nepes AI, a business unit of nepes (South Korea), launches the NM500 chip, the smallest neuromorphic IC available on the market and featuring no less than a NeuroMem neural network with 576 neurons. The chip can be integrated into sensors and IOT platforms to deploy pattern learning and recognition at the edge. Conveniently packaged in a wafer scale package, the chip delivers 120,000 pattern recognitions per second for less than 125 mW. Early adopters can put the neurons into use immediately with nepes’ NeuroShield module interfacing to the low-cost Arduino microcontroller boards through SPI.
DF Robot is shipping a CurieNeurons Kit , a toolbox for makers who want to have a shot at adding AI to their IoT projects. The kit includes an Arduino/Genuino101 board and a collection of sensors and actuators. Using the General Vision CurieNeurons Pro library, Arduino developers can train the neurons inside the Curie chipset by showing examples and query them to classify signals without worrying about the technical nuances of how neural networks actually work.
