Abstract: A physiological data acquisition system includes an array electrode sensor (220) having plural electrodes and configured to acquire physiological data; a single electrode sensor having a single electrode and configured to acquire additional physiological data; and a hub that is configured to receive the physiological data from the array electrode sensor and the additional physiological data from the single electrode sensor only along body communication channels. At least one of the array electrode sensor and the single electrode sensor is configured to send an energy request signal to the hub, along the body communication channels. The hub, in response to the received energy request signal, emits radio frequency signals, which are used by the at least one of the array electrode sensor and the single electrode sensor to harvest energy.

Abstract: A stuck-at fault mitigation method for resistive random access memory (ReRAM)-based deep learning accelerators, includes: confirming a distorted output value (Y0) due to a stuck-at fault (SAF) by using a correction data set in a pre-trained deep learning network, by means of ReRAM-based deep learning accelerator hardware; updating an average (?) and a standard deviation (?) of a batch normalization (BN) layer by using the distorted output value (Y0), by means of the ReRAM-based deep learning accelerator hardware; folding the batch normalization (BN) layer in which the average (?) and the standard deviation (?) are updated into a convolution layer or a fully-connected layer, by means of the ReRAM-based deep learning accelerator hardware; and deriving a normal output value (Y1) by using the deep learning network in which the batch normalization (BN) layer is folded, by means of the ReRAM-based deep learning accelerator hardware.

Abstract: Disclosed are various approaches for a controller that can generate and use non-stationary polar codes for encoding and decoding information. In one example, a method includes performing, by an encoder of the controller, a linear operation on at least one vector of information to be stored in a memory. The linear operation includes generating a polar encoded representation from the at least one vector of information. The linear operation also includes generating an output using at least one permutation that is based on a statistical characterization analysis of channels of the memory and a channel dependent permutation that is applied to the polar encoded representation. In some aspects, the statistical characterization analysis includes a respective reliability level of each one of the plurality of channels, and the channel dependent permutation includes an ordered permutation that orders the channels according to their respective reliability level.

Abstract: A stuck-at fault mitigation method for resistive random access memory (ReRAM)-based deep learning accelerators, includes: confirming a distorted output value (Y0) due to a stuck-at fault (SAF) by using a correction data set in a pre-trained deep learning network, by means of ReRAM-based deep learning accelerator hardware; updating an average (?) and a standard deviation (?) of a batch normalization (BN) layer by using the distorted output value (Y0), by means of the ReRAM-based deep learning accelerator hardware; folding the batch normalization (BN) layer in which the average (?) and the standard deviation (?) are updated into a convolution layer or a fully-connected layer, by means of the ReRAM-based deep learning accelerator hardware; and deriving a normal output value (Y1) by using the deep learning network in which the batch normalization (BN) layer is folded, by means of the ReRAM-based deep learning accelerator hardware.

Abstract: Disclosed are various approaches for a controller that can generate and use non-stationary polar codes for encoding and decoding information. In one example, a method includes performing, by an encoder of the controller, a linear operation on at least one vector of information to be stored in a memory. The linear operation includes generating a polar encoded representation from the at least one vector of information. The linear operation also includes generating an output using at least one permutation that is based on a statistical characterization analysis of channels of the memory and a channel dependent permutation that is applied to the polar encoded representation. In some aspects, the statistical characterization analysis includes a respective reliability level of each one of the plurality of channels, and the channel dependent permutation includes an ordered permutation that orders the channels according to their respective reliability level.

Abstract: A power management technique utilizing a method for accurately and rapidly estimating the change in the statistical distribution of data at each block in a communication system leading to or originating from a memory that is experiencing voltage scaling induced errors is disclosed. An appropriate memory supply voltage that maximizes power savings is found by exploiting the available SNR slack while keeping system performance within a required margin.

Abstract: A power management technique utilizing a method for accurately and rapidly estimating the change in the statistical distribution of data at each block in a communication system leading to or originating from a memory that is experiencing voltage scaling induced errors is disclosed. An appropriate memory supply voltage that maximizes power savings is found by exploiting the available SNR slack while keeping system performance within a required margin.