Learn on the chip, or load models previously learned and saved by the same or other chip
It's Real Time
Recognize patterns in micro-seconds regardless of the number of models stored in the neurons.
It's Energy Efficient
100,000 recognitions per second for milliWatts per 1,000 models stored in the neurons
It's Available NOW
Available on ASICs, SOCs, and FPGAs and can be evaluated on a variety of platforms
GV Company News
nepes, leader in advanced semiconductor assembly and packaging technologies, and General Vision (GV), leader in the design of digital neural network and low-power AI components, have signed an agreement to develop and manufacture a new wafer chip-scale packaged (WCSP)...read more
Neuromorphic computing and NeuroMem explained simply...Read it from The National Museum of Computing newsletter (page 12), also featuring our famous off-shore fish inspection with less than 100...read more
Maker Collider is introducing its CurieNeurons Kit, an easy-to-use tool for makers of all levels who want to have a shot at building artificial intelligence related IoT projects. It enables users to build smart hardware capable of pattern learning and classification...read more
Design IoT applications and smart non-connected appliances integrating autonomous pattern learning and recognition with 1024 silicon neurons. Easy to program through the Arduino IDE, Eclipse or else, the BrainCard can be interfaced to a wealth of sensors and actuators.
Develop applications which can learn and classify patterns at high speed whether they come from text, network packets, images, audio signal, biosensors, and more. The expandable network of 4096 silicon neurons can receive patterns from the USB port or directly from the reconfigurable on-board FPGA.