Abstract: A processor includes a synchronous circuit including a plurality of processing stages, wherein each processing stage includes a selection data bus; and an asynchronous circuit coupled to each selection data bus, wherein the asynchronous circuit includes an asynchronous state machine whose states correspond to a process phase or a plurality of circuits, wherein the asynchronous circuit further includes a selectable delay circuit whose delay is determined by a present state of the asynchronous state machine, and wherein the asynchronous circuit is configured for generating a plurality of processing stage clock signals each having a selectable delay provided by the selectable delay circuit.

Abstract: A sensor device includes an infrared sensor configured to generate sensor data. The sensor device also includes a configurable digital analysis block. The configurable digital analysis block is configured to generate classification data based on the sensor data. The configurable digital analysis block includes a plurality of selectable analysis blocks that can be selectively included in generating the classification data.

Abstract: A processor includes a synchronous circuit including a plurality of processing stages, wherein each processing stage includes a selection data bus; and an asynchronous circuit coupled to each selection data bus, wherein the asynchronous circuit includes an asynchronous state machine whose states correspond to a process phase or a plurality of circuits, wherein the asynchronous circuit further includes a selectable delay circuit whose delay is determined by a present state of the asynchronous state machine, and wherein the asynchronous circuit is configured for generating a plurality of processing stage clock signals each having a selectable delay provided by the selectable delay circuit.

Abstract: A device includes a multiplier, an accumulator and a floating point adder. The multiplier generates a product of a first factor having a sign bit and exponent bits and a second factor having a sign bit and exponent bits. The multiplier includes a sign multiplier and a subtractor. The sign multiplier generates a product of the sign bit of the first factor and the sign bit of the second factor. The subtractor subtracts the exponent bits of the first factor from the exponent bits of the second factor. The accumulator stores a current accumulation value. The floating-point adder is coupled to the multiplier and to the accumulator, and, in operation, the adder generates an updated accumulation value based a sum of the product and the current accumulation value, and stores the updated accumulation value in the accumulator. The first factor may be a weight of a neural network.

Abstract: A device for monitoring the health state is made in a chip including a semiconductor die integrating an electric potential sensor and a cardiac parameter determination unit. The potential sensor is configured to detect potential variations on the body of a living being and associated with a heart rhythm and to generate a cardiac signal. The cardiac parameter determination unit is configured to receive the cardiac signal and determine cardiac parameters indicative of a health state. In particular, the cardiac parameter determination unit is configured to detect triggering events and to determine features of the cardiac signal in time windows defined by the triggering events. The die also integrates a decision unit, configured to receive the cardiac parameters and generate a health signal based on a comparison with threshold values. The cardiac parameters include heart rate and QRS-complex.

Abstract: System, method, and circuitry for utilizing sequential input inertial sensor data to calculate recursive features for training a machine learning algorithm or for classifying the data as a known class. The recursive feature values of a current data sample are calculating based on comparisons between the current data sample value and previous recursive feature values. The recursive features include a recursive maximum, recursive minimum, recursive peak to peak, recursive average, recursive root mean square, and recursive variance.