Neural processing unit for cardiovascular disease prediction artificial neural network model

A neural processing unit (NPU) implemented in hardware, the NPU comprising:

• a controller configured to receive a machine code of an artificial neural network (ANN) model for predicting cardiovascular disease, the ANN configured to input sensing data of at least one of ECG, respiration, pulse rate, acceleration, and body temperature and to output a probability of disease onset for at least one disease among cerebral infarction, heart failure, and ischemic heart disease, the sensing data being input from M heterogeneous sensors;
• a processing element (PE) array formed as an N×M matrix and configured to perform computation of the ANN model based on the machine code, the ANN model including computation of at least one fusion layer that combines multiple sensing data to output fused data of the M heterogeneous sensors;
• and
• a special function unit (SFU) configured to compute a corresponding special function by receiving a convolution operation value processed by the PE array,
• wherein the SFU includes a plurality of function units and is further configured to selectively control at least one of the plurality of function units in a sequence optimized according to data locality information of the ANN model included in the machine code,
• wherein the controller is further configured to selectively delete non-reusable data during the computation of the at least one fusion layer based on the memory data locality information of the ANN model, and
• wherein the controller is further configured such that
• a size of data of one of the M heterogeneous sensors is scaled up or scaled down so that data sizes of one axis of the fused data of the M heterogeneous sensors are equal, and
• the PE array has a utilization rate that varies according to a size of at least one axis of heterogeneous sensor data.

A system comprising:

• at least one neural processing unit (NPU) implemented in hardware, the NPU including
• a controller configured to receive a machine code of an artificial neural network (ANN) model for predicting cardiovascular disease, the ANN model configured to input sensing data of at least one of ECG, respiration, pulse rate, acceleration, and body temperature and to output a probability of disease onset for at least one disease among cerebral infarction, heart failure, and ischemic heart disease,
• an input unit configured to receive a plurality of input signals from M heterogeneous sensors corresponding to the ANN model,
• a processing element array formed as an N×M matrix and configured to perform a convolution operation including computation of at least one fusion layer that combines multiple sensing data to output fused data of the M heterogeneous sensors, and
• an on-chip memory configured to store a result of the convolution operation;
• and
• a memory controller including a memory, the memory controller configured to
• receive data locality information of the ANN model, the data locality information of the ANN model predicting successive memory operation requests in an optimized sequence of the at least one neural processing unit, and
• cache a next memory operation request to be requested by a corresponding one of the at least one neural processing unit based on the data locality information,
• wherein the controller is further configured to selectively delete non-reusable data during the computation of the at least one fusion layer based on the on-chip memory data locality information of the ANN model, and
• wherein the controller is further configured such that
• a size of data of one of the M heterogeneous sensors is scaled up or scaled down so that data sizes of one axis of the fused data of the M heterogeneous sensors are equal, and
• the PE array has a utilization rate that varies according to a size of at least one axis of heterogeneous sensor data.