Abstract: Methods, systems, apparatuses, and computer program products are provided herein for determining the handedness of input provided by a user via a touch interface. For instance, for each touch-based input detected, a score indicating a probability whether the touch-based input was inputted by a particular hand of the user is generated. A classification for the touch-based input is then generated based on a drift diffusion model-based technique in which inter-dependencies between a series of touch-based input are approximated. The determined classifications are used to determine the handedness of the user.

Abstract: Embodiments described herein are directed to training techniques to reduce the power consumption and decrease the inference time of an NN. For example, during training, an estimate of power consumed by AMACs of a hardware accelerator on which the NN executes during inferencing is determined. The estimate is based at least on the non-zero midterms generated by the AMACs and the precision thereof. A loss function of the NN is modified such that it formulates the non-zero midterms and the precision thereof. The training forces the modified loss function to generate a sparse bit representation of the weights of the NN and to reduce the precision of the AMACs. Noise may also be injected at the output of nodes of the NN that emulates noise generated at an output of the AMACs. This enables the weights to account for the intrinsic noise that is experienced by the AMACs during inference.

Abstract: Methods, systems, apparatuses, and computer program products are provided herein for determining the handedness of input provided by a user via a touch interface. For instance, for each touch-based input detected, a score indicating a probability whether the touch-based input was inputted by a particular hand of the user is generated. A classification for the touch-based input is then generated based on a drift diffusion model-based technique in which inter-dependencies between a series of touch-based input are approximated. The determined classifications are used to determine the handedness of the user.

Abstract: Methods, systems, apparatuses, and computer program products are provided herein for determining the handedness of input provided by a user via a touch interface. For instance, for each touch-based input detected, a score indicating a probability whether the touch-based input was inputted by a particular hand of the user is generated. A classification for the touch-based input is then generated based on a drift diffusion model-based technique in which inter-dependencies between a series of touch-based input are approximated. The determined classifications are aggregated and compared to threshold(s) to determine the handedness of the user. For example, if the aggregated classifications meet a first threshold, then a determination is made that touch-based input provided by a user was inputted by the user's left hand. If the aggregated classifications meet a second threshold, then a determination is made that touch-based input provided by the user was inputted by the user's right hand.