Abstract: A method of constraining data represented in a deep neural network is described. The method includes determining an initial shifting specified to convert a fixed-point input value to a floating-point output value. The method also includes determining an additional shifting specified to constrain a dynamic range during converting of the fixed-point input value to the floating-point output value. The method further includes performing both the initial shifting and the additional shifting together to form a dynamic, range constrained, normalized floating-point output value.

Abstract: A method of constraining data represented in a deep neural network is described. The method includes determining an initial shifting specified to convert a fixed-point input value to a floating-point output value. The method also includes determining an additional shifting specified to constrain a dynamic range during converting of the fixed-point input value to the floating-point output value. The method further includes performing both the initial shifting and the additional shifting together to form a dynamic, range constrained, normalized floating-point output value.

Abstract: Approaches are described to improve database performance by implementing a RLE decompression function at a low level within a general-purpose processor or an external block. Specifically, embodiments of a hardware implementation of an instruction for RLE decompression are disclosed. The described approaches improve performance by supporting the RLE decompression function within a processor and/or external block. Specifically, a RLE decompression hardware implementation is disclosed that produces a 64-bit RLE decompression result, with an example embodiment performing the task in two pipelined execution stages with a throughput of one per cycle. According to embodiments, hardware organization of narrow-width shifters operating in parallel, controlled by computed shift counts, is used to perform the decompression.

Abstract: A processor includes an integer multiplier configured to execute an integer multiply instruction to multiply significand bits of at least one floating point operand of a floating point multiply operation. The processor also includes a floating point multiplier configured to execute a special purpose floating point multiply accumulate instruction with respect to an intermediate result of the floating point multiply operation and the at least one floating point operand to generate a final floating point multiplication result.

Abstract: A processor includes an integer multiplier configured to execute an integer multiply instruction to multiply significand bits of at least one floating point operand of a floating point multiply operation. The processor also includes a floating point multiplier configured to execute a special purpose floating point multiply accumulate instruction with respect to an intermediate result of the floating point multiply operation and the at least one floating point operand to generate a final floating point multiplication result.

Abstract: Approaches are described to improve database performance by implementing a RLE decompression function at a low level within a general-purpose processor or an external block. Specifically, embodiments of a hardware implementation of an instruction for RLE decompression are disclosed. The described approaches improve performance by supporting the RLE decompression function within a processor and/or external block. Specifically, a RLE decompression hardware implementation is disclosed that produces a 64-bit RLE decompression result, with an example embodiment performing the task in two pipelined execution stages with a throughput of one per cycle. According to embodiments, hardware organization of narrow-width shifters operating in parallel, controlled by computed shift counts, is used to perform the decompression.

Abstract: Embodiments of an apparatus are disclosed for performing arithmetic operations on provided operands. The apparatus may include a fetch unit, and an arithmetic logic unit (ALU). The fetch unit may be configured to retrieve two operands responsive to receiving an instruction, wherein the operands include binary-coded decimal values. The ALU may be configured to scale a value of each of the operands, and then compress the scaled values of the operands. The compressed values of the operands may include fewer data bits than the corresponding scaled values. The ALU may be further configured to estimate a portion of a result of the operation dependent upon the compressed values of the operands.

Abstract: An operand rotator (100) and method of rotating an operand is disclosed. The operand rotator (100) includes a first decoder (102) with a first input to receive an operand size indicating one of a plurality of operand sizes, a second input for receiving a rotate amount signal and a control output to provide a plurality of control signals. The operand rotator (100) also includes a rotator (104) with a first input coupled to the control output of the first decoder (102), a second input to receive a data element and an output to provide rotated data. The rotator (104) is responsive to the plurality of control signals to rotate portions of the data element corresponding to one of the plurality of operand sizes by an amount corresponding to the rotate amount signal.