Implementations of the disclosure provide logarithm and anti-logarithm operations on a hardware processor based on linear piecewise approximation. An example processor includes a piece wise linear log approximation circuit that receives an input of a floating-point number comprising a sign, an exponent and a mantissa. The piece wise linear log approximation circuit approximates a fractional portion of a fixed point number using a linear approximation of the mantissa of the floating-point number. The piece wise linear log approximation circuit also derives an integer from the exponent.

Systems and methods for efficient fixed-base multi-precision exponentiation are disclosed herein. An example method includes applying a multi-precision exponentiation algorithm to a base number, the multi-precision exponentiation algorithm comprises a pre-generated lookup table used to perform calculations on the base number, the pre-generated lookup table comprising pre-calculated exponentiated values of the base number.

Systems and methods for end-to-end encryption of a web browsing process are described herein. A web query is encrypted at a client using a homomorphic encryption scheme. The encrypted query is sent to a server where the encrypted query is evaluated over web content to generate an encrypted response without decrypting the encrypted query and without decrypting the response. The encrypted response is sent to the client where it is decrypted to obtain the results of the query without revealing the query or results to the owner of the web content, an observer, or an attacker.

Systems and methods for end-to-end encryption and compression are described herein. A query is encrypted at a client using a homomorphic encryption scheme. The encrypted query is sent to a server where the encrypted query is evaluated over target data to generate encrypted response without decrypting the encrypted query. The result elements of the encrypted response are grouped, co-located, and compressed, without decrypting the encrypted query or the encrypted response. The compressed encrypted response is sent to the client where it is decrypted and decompressed to obtain the results of the query without revealing the query or results to the owner of the target data, an observer, or an attacker.

A method is provided for solving a computational problem that is reducible to a problem of counting solutions to an associated decision problem. The method includes estimating a number of the solutions to the decision problem using a quantum computer by determining if there is at least one simultaneous solution to both (i) the decision problem and (ii) an associated hashing problem. The method also includes increasing a precision of the estimated number of the solutions to the decision problem using the quantum computer by determining if there are multiple solutions to the decision problem that are simultaneously solutions to the associated hashing problem. The method further includes outputting or using the estimated number of the solutions to the decision problem as a solution to the computational problem.

A processing element is configured to approximate a transcendental function. The processing element comprises an input storage and a look-up storage. The processing element obtains floating-point input data from the input storage representing having an input exponent value and an input mantissa value. The processing element looks up approximation parameters and an output exponent value from the look-up storage, wherein each group of approximation parameters and output exponent value are stored in the look-up storage in association with a respective range of a plurality of ranges that are defined by the input exponent value and the input mantissa value. The ranges cover values of the input exponent value and input mantissa value such that the output exponent value associated with each range does not change by more than a predetermined number. An approximation function is evaluated that approximates the transcendental function based on the looked-up approximation parameters and output exponent.

A hardware acceleration circuit, a data processing acceleration method, a chip, and an accelerator are provided. The hardware acceleration circuit includes: an exponential function module, configured to obtain exponential function values of a plurality of data elements in a data set; an adder, configured to obtain an addition operation result of the exponential function values; a first processing circuit, configured to perform preset processing on the addition operation result, to process the addition operation result into at least first data and second data; a second processing circuit, configured to perform preset processing on at least the first data and the second data, to obtain a reciprocal of the addition operation result; and a third processing circuit, configured to perform preset processing on an exponential function value of an i.sup.th data element in the data elements and the reciprocal, to obtain a specific function value of the i.sup.th data element.

Disclosed are a method and apparatus for accelerating inference of a neural network model, an electronic device, and a medium. The method includes: acquiring image training data, text training data, or speech training data; determining a first neural network model to be accelerated; converting a preset operation on a preset network layer in the first neural network model to a first operation for simulating operational logic of a target operation to obtain a second neural network model; performing, based on the image training data, the text training data, or the speech training data, quantization aware training on the second neural network model by a preset bit width to obtain a third neural network model which is quantized; and converting the first operation of the third neural network model to the target operation, to obtain a target neural network model, which is accelerated, corresponding to the first neural network model.

Disclosed are exponential function calculation method, exponential function calculation system, and exponential function calculation circuit for calculating an exponential function value of an input value. The exponential function calculation method includes: dividing the input value as a sum of an integer value and a decimal value; representing the integer value by an integer bit string with a first length, and representing the decimal value by a decimal bit string with a second length; looking up a first table to obtain a first value corresponding to an exponent function value of the integer value according to the integer bit string, and interpolating to obtain an interpolation value corresponding to an exponent function value of the decimal value according to the decimal bit string; and calculating an output value corresponding to an exponent function value of the input value according to a product of the first value and the interpolation value.