Abstract: A processing device comprising a plurality of operand registers, wherein a first subset of the operand registers are configured to store state information for a plurality of bins, comprising a range of values and a bin count associated with each respective bin, wherein a second subset of the operand registers is configured to store a vector of floating-point values; and an execution unit configured to execute a first instruction taking the state information for the plurality of bins and the vector of floating-point values as operands, and in response to execution of the first instruction, for each of the floating-point values: identify based on an exponent of the respective floating-point value, each one of the plurality of bins for which the respective floating-point value falls within the associated range of values; and increment the bin count associated with the identified bins.

Abstract: A processing device comprising: a control register configured to store a scaling factor; at least one execution unit configured to execute instructions to perform arithmetic operations on input floating-point numbers provided according to a first floating-point format, wherein each of the input floating-point numbers provided according to the first floating-point format comprises a predetermined number of bits, wherein the at least one execution unit is configured to, in response to execution of an instance of a first of the instructions: perform processing of a first set of the input floating-point numbers to generate a result value, the result value provided in a further format and comprising more the predetermined number of bits, enabling representation of a greater range of values than is representable in the first floating-point format; and apply the scaling factor specified in the control register to increase or decrease an exponent of the result value.

Abstract: A computer-implemented method of training a multi-layer neural network comprising a set of network weights, comprising: processing the training data in respective forward and backward passes through multiple layers, the forward pass comprising computing a set of activations in dependence on the network weights and training data, and the backward pass comprising: computing gradients of a pre-determined loss function with respect to the network weights and/or activations, wherein an adjustment parameter is applied to at least a subset of values in the neural network, the values comprising at least one of: the network weights, the activations, the gradients with respect to activations and the gradients with respect to weights; updating the network weights in dependence on the computed gradients; computing a proportion of the subset of values falling above a predefined threshold; and updating the adjustment parameter in dependence on the computed proportion.

Abstract: A computer-implemented method comprising: processing data in a neural network to compute a network tensor comprising a plurality of tensor elements represented in an initial numerical format; computing a histogram of tensor elements; selecting a target numerical format, the target numerical format having a lower precision than the initial numerical format; evaluating a metric based on the histogram of tensor elements and the target numerical format, the metric indicating a degree of accuracy of a representation of the network tensor in the target numerical format; and based on the evaluated metric, converting the plurality of tensor elements from the initial numerical format to the target numerical format.

Abstract: A computer comprising a plurality of processing units, each processing unit having an execution unit and access to computer memory which stores code executable by the execution unit and input values of an input vector to be processed by the code, the code, when executed, configured to access the computer memory to obtain multiple pairs of input values of the input vector, determine a maximum or corrected maximum input value of each pair as a maximum result element, determine and store in a computer memory a maximum or corrected maximum result of each pair of maximum result elements as an approximation to the natural log of the sum of the exponents of the input values and access the computer memory to obtain each input value and apply it to the maximum or corrected maximum result to generate each output value of a Softmax output vector.

Abstract: A wireless communications receiver includes a receiving unit configured to receive a radio transmission having Doppler distortion and channel noise. Also included is an estimating unit coupled to the receiving unit and configured to determine a noise-abated maximum Doppler frequency estimate for the Doppler distortion. Additionally, the estimating unit is further configured to determine a signal-to-noise ratio (SNR) estimate based on the noise-abated maximum Doppler frequency estimate. In another aspect, a method of operating a wireless communications receiver is provided.

Abstract: A wireless communications receiver includes a receiving unit configured to receive a radio transmission having Doppler distortion and channel noise. Also included is an estimating unit coupled to the receiving unit and configured to determine a noise-abated maximum Doppler frequency estimate for the Doppler distortion. Additionally, the estimating unit is further configured to determine a signal-to-noise ratio (SNR) estimate based on the noise-abated maximum Doppler frequency estimate. In another aspect, a method of operating a wireless communications receiver is provided.