An adder circuit that includes an operand input and a second operand input to an XNOR cell. The XNOR cell is configured to provide the operand input and the second operand input to both a NAND gate and a first OAI cell. A second OAI cell transforms the output of the XNOR cell into a carry out signal.

An apparatus and method for efficiently performing a multiply add or multiply accumulate operation. For example, one embodiment of a processor comprises: a decoder to decode an instruction specifying an operation, the instruction comprising a first operand identifying a multiplier and a second operand identifying a multiplicand; and fused multiply-add (FMA) execution circuitry comprising first multiplication circuitry to perform a multiplication using the multiplicand and multiplier to generate a result for multipliers and multiplicands falling within a first precision range, and second multiplication circuitry to be used instead of the first multiplication circuitry for multipliers and multiplicands falling within a second precision range.

An apparatus and method for efficiently performing a multiply add or multiply accumulate operation. For example, one embodiment of a processor comprises: a decoder to decode an instruction specifying an operation, the instruction comprising a first operand identifying a multiplier and a second operand identifying a multiplicand; and fused multiply-add (FMA) execution circuitry comprising first multiplication circuitry to perform a multiplication using the multiplicand and multiplier to generate a result for multipliers and multiplicands falling within a first precision range, and second multiplication circuitry to be used instead of the first multiplication circuitry for multipliers and multiplicands falling within a second precision range.

A processor-implemented method implementing a convolution neural network includes: determining a plurality of differential groups by grouping a plurality of raw windows of an input feature map into the plurality of differential groups; determining differential windows by performing, for each respective differential group of the differential groups, a differential operation between the raw windows of the respective differential group; determining a reference element of an output feature map corresponding to a reference raw window among the raw windows by performing a convolution operation between a kernel and the reference raw window; and determining remaining elements of the output feature map by performing a reference element summation operation based on the reference element and each of a plurality of convolution operation results determined by performing respective convolution operations between the kernel and each of the differential windows.

A processor-implemented method implementing a convolution neural network includes: determining a plurality of differential groups by grouping a plurality of raw windows of an input feature map into the plurality of differential groups; determining differential windows by performing, for each respective differential group of the differential groups, a differential operation between the raw windows of the respective differential group; determining a reference element of an output feature map corresponding to a reference raw window among the raw windows by performing a convolution operation between a kernel and the reference raw window; and determining remaining elements of the output feature map by performing a reference element summation operation based on the reference element and each of a plurality of convolution operation results determined by performing respective convolution operations between the kernel and each of the differential windows.

Certain aspects provide methods and apparatus for multiplication of digital signals. In accordance with certain aspects, a multiplication circuit may be used to multiply a portion of a first digital input signal with a portion of a second digital input signal via a first multiplier circuit to generate a first multiplication signal, and multiply another portion of the first digital input signal with another portion of the second digital input signal via a second multiplier circuit to generate a second multiplication signal. A third multiplier circuit and multiple adder circuits may be used to generate an output of the multiplication circuit based on the first and second multiplication signals.

An ALU is capable of generating a multiply accumulation by compressing like-magnitude partial products. Given N pairs of multiplier and multiplicand, Booth encoding is used to encode the multipliers into M digits, and M partial products are produced for each pair of with each partial product in a smaller precision than a final product. The partial products resulting from the same encoded multiplier digit position, are summed across all the multiplies to produce a summed partial product. In this manner, the partial product summation operations can be advantageously performed in the smaller precision. The M summed partial products are then summed together with an aggregated fixup vector for sign extension. If the N multipliers equal to a constant, a preliminary fixup vector can be generated based on a predetermined value with adjustment on particular bits, where the predetermined value is determined by the signs of the encoded multiplier digits.

According to the embodiments, a semiconductor device includes: an adder configured to generate positive multiple data of the multiplicand which is used for a plurality of the multiplication in plurality and does not include a value of 2.sup.n (n is a positive integer) of the multiplicand; a Wallace tree circuit provided in each of the multiplier circuits and configured to operate a sum of a plurality of partial products by using a plurality of adders; and a selection circuit provided in each of the multiplier circuits and configured to select, according to a plurality of bits selected from the multiplier, data falling in a multiple of one of the multiplicand, data of 2.sup.n of the multiplicand, and the positive multiple data from the adder in order to output as one partial product of the plurality of partial products to the Wallace tree circuit.

In an example, an apparatus comprises a plurality of execution units and logic, at least partially including hardware logic, to gate at least one of a multiply unit or an accumulate unit in response to an input of value zero. Other embodiments are also disclosed and claimed.

Systems, methods, and devices for enhancing performance/efficiency of soft multiplier implementations are provided. More specifically, a method to implement soft multipliers with a high radix subset code architecture is provided. The techniques provided herein result in smaller multipliers that consume less area, improve packing, consume less power, and improve routing options on an integrated circuit.