The CogniMem Evaluation Base Board (CM-EB) offers developers and OEMs a comprehensive platform to evaluate the CogniMem neural network technology for the real-time recognition of data coming from sensors, instruments, or else. Typical applications include machine vision, face recognition, voice and signal recognition, but also data mining. The board features 2 CogniMem chips, each with 1024 neurons, an Actel FPGA accessible to programmers and a digital input bus for easy connectivity to external sensors. User I/O lines include an I2C serial bus, an RS232 bus and 8 uncommitted general purpose I/Os brought to header pins.
The CogniMem neural network implements two powerful non-linear classifiers (RCE and KNN) in a natively parallel architecture. The tremendous benefit of this architecture is a recognition cycle which remains under 11 us whether the entire network is composed of 1, 2 or more chips. Brute computational power is equivalent to 80 gig operations/second @ 27 MHz for one chip, twice as many for two chips, etc. The CogniMem neurons build their knowledge by learning example vectors and their associated category. This can be done in real-time or a pre-existing knowledge can be loaded in advance from file or Flash memory.
