A circuit system for performing modular reduction of a modular multiplication includes multiplier circuits that receive a first subset of coefficients that are generated by summing partial products of a multiplication operation that is part of the modular multiplication. The multiplier circuits multiply the coefficients in the first subset by constants that equal remainders of divisions to generate products. Adder circuits add a second subset of the coefficients and segments of bits of the products that are aligned with respective ones of the second subset of the coefficients to generate sums.

Embodiments of a telemetry device and methods to convert a binary floating point number to a compressed number is described herein. The binary floating point number may comprise a mantissa and an exponent. The telemetry device may determine a first number based on a product of the exponent and a constant, wherein the constant may be proportional to a logarithm of the number two. The telemetry device may determine a second number using one or more bits of the mantissa as an index into a predetermined lookup table. Values of the lookup table may be proportional to logarithms of candidate mantissa values. The telemetry device may determine the compressed number based on rounding of a sum. The sum may include the first and second numbers. The rounding may be based on a predetermined step size.

A processing unit to support inference acceleration for machine learning (ML) comprises an inline post processing unit configured to accept and maintain one or more lookup tables for performing each of one or more non-linear mathematical operations. The inline post processing unit is further configured to accept data from a set of registers maintaining output from a processing block instead of streaming the data from an on-chip memory (OCM), perform the one or more non-linear mathematical operations on elements of the data from the processing block via their corresponding lookup tables, and stream post processing result of the one or more non-linear mathematical operations back to the OCM after the one or more non-linear mathematical operations are complete.

A calculation processor for determining a digital output value from a digital input value based on an exponent value a, the processor comprising a first calculation block, a second calculation block and a final calculation block. The first calculation block initializes an intermediate value and an error value depending on a position of a Most Significant Bit of a significant part of the input value. The second calculation block is configured to perform repeatedly, until an exit criterion is fulfilled, the incrementation of a counter value, the determination of a power error value based on the error value and, if the power error value is larger than or equal to an error threshold, adjustment of the intermediate value y by multiplying the intermediate value with an adaptation value and setting the error value to the power error value divided by the base value. If the power error value is smaller than the error threshold, the error value is set to the power error value. The final calculation block is configured to set the output value to the intermediate value.

Certain aspects of the present disclosure provide a method for processing input data by a configurable nonlinear activation function circuit, including determining a nonlinear activation function for application to input data; determining, based on the determined nonlinear activation function, a set of parameters for a configurable nonlinear activation function circuit; and processing input data with the configurable nonlinear activation function circuit based on the set of parameters to generate output data.

A system performs big number multiplication during a cryptographic process. This can occur, for example, when a controller in a storage system encrypts data for storage in its memory or decrypts data read from its memory. To perform the multiplication of these big input numbers quickly, the system uses a modified Toom-Cook algorithm comprising a plurality of levels of coefficient vectors for each of the input numbers. This involves performing a sample extraction process, a point multiplication process, and an interpolation (synthesis) process.

Certain aspects of the present disclosure provide a method for processing input data by a set of configurable nonlinear activation function circuits, including generating an exponent output by processing input data using one or more first configurable nonlinear activation function circuits configured to perform an exponential function, summing the exponent output of the one or more first configurable nonlinear activation function circuits, and generating an approximated log softmax output by processing the summed exponent output using a second configurable nonlinear activation function circuit configured to perform a natural logarithm function.

Embodiments of the present invention may provide the capability to evaluate logarithm and power (exponentiation) functions using either hardware specific instructions, or a hardware specific implementation with reduced memory requirements. An input comprising a floating point representation of a real number may be received and a mantissa and an exponent may be extracted. A function of a logarithm of a mantissa of the real number may be approximated by utilizing a polynomial based on the mantissa. The approximated function of the logarithm may be combined with the exponent for calculating a value comprising a logarithm of the real number. Likewise, an input comprising a floating point representation of a real number and a representation of a second number may be received and an approximation of the real number to the power of the second number may be generated.

A calculation processor for determining a digital output value (OUT) from a digital input value (IN) based on an exponent value a, the processor comprising a first calculation block (CB1), a second calculation block (CB2) and a final calculation block (CBF). The first calculation block (CB1) initializes an intermediate value and an error value depending on a position of a Most Significant Bit of a significant part of the input value. The second calculation block is configured to perform repeatedly, until an exit criterion is fulfilled, the incrementation of a counter value, the determination of a power error value based on the error value and, if the power error value is larger than or equal to an error threshold, adjustment of the intermediate value y by multiplying the intermediate value with an adaptation value and setting the error value to the power error value divided by the base value. If the power error value is smaller than the error threshold, the error value is set to the power error value. The final calculation block is configured to set the output value to the intermediate value.

A digital signal processing device is provided. The digital signal processing devices includes: a processor configured to execute instructions to implement: a lookup table generator configured to generate a first lookup table corresponding to a first exponential function, based on an input scaling value; and a softmax calculator configured to receive input data indicating input values, calculate a first index of the first lookup table, the first index corresponding to a first input value of the input values, read a first exponential function value corresponding to the first index from the first lookup table, calculate a first intermediate value based on the first exponential function value and the first input value, and generate output data indicating output values respectively corresponding to the input values, wherein a first output value of the output values is generated based on the first intermediate value.