AI2GO enables this vision through a platform of a large number of models, running on a large number of devices, that are able to operate under a large number of constraints," says Ali Farhadi, Co-founder and CXO, Xnor.
First the user selects their preferred hardware (Raspberry Pi, Linux, Ambarella, Toradex etc.), then chooses an AI use case, for example a "pet classifier for a home security camera," a "person detector for a dash cam," or a "person segmenter for video conferencing applications." Because AI2GO models are designed to run in resource-constrained environments, Xnor provides the user with the novel opportunity to tune their model for latency (milliseconds) and memory footprint (megabytes) in order to fit within the user's set of constraints.
Xnor will be hosting events to on-board new users to AI2GO in the coming months including at the Embedded Vision Summit in California, May 20-23, in booth #419.
Xnor is dedicated to accelerating AI and deep learning in consumer and business devices.
Alternatively, the XNOR solution might emerge from interactions of the Mauthner network as a whole.
How might the PHP cells be performing part of the XNOR function?
We are currently doing neurophysiological and behavioral experiments to evaluate the XNOR model.
We are currently investigating Mauthner responsiveness to acceleration alone, and to combinations of acceleration and pressure, to determine whether the Mauthner cell itself performs the XNOR function.
The modified XNOR circuit where the power consumption is reduced significantly by deliberate use of weak inverter (channel width of transistors being small) formed by transistors Mp1 and Mn1.
The XNOR module employed six transistors (6T) (three PMOS transistors and three NMOS transistors).
Modified XNOR module and carry generation module circuit is designed individually by using p-channel metal oxide semiconductor (PMOS) and n-channel metal oxide semiconductor (NMOS) transistors.