Abstract: The present disclosure relates to a floating-point computation circuit comprising: an internal memory (104, 114) storing one or more floating-point values in a first format; status registers (124) defining a plurality of floating-point number format types associated with corresponding identifiers, each format type indicating at least a maximum size (BIS, MBB); and a load and store unit (108, 118) for loading floating-point values from and storing floating-point values to an external memory (120, 122), the load and store unit (108, 118) being configured: to receive, in relation with a first store operation, a first floating-point value from the internal memory (104, 114) and a first of said identifiers; and to convert the first floating-point value from the first format to a first external memory format having a maximum size (BIS, MBB) defined by the floating-point number format type designated by the first identifier.

Abstract: Provided are an integrated circuit chip apparatus and a related product, the integrated circuit chip apparatus being used for executing a multiplication operation, a convolution operation or a training operation of a neural network. The present technical solution has the advantages of a small amount of calculation and low power consumption.

Abstract: A method may include parsing, using a JavaScript Object Notation (JSON) parser implemented on a field programmable gate array (FPGA), a JSON document. The parsing includes dividing an input string comprising the JSON document into one or more data blocks and annotating the characters included in each data block a bitmap for each data block. String characters included in the data blocks may be identified, based on the bitmap associated with each data block, for writing to a string array. Numeric characters included in the data blocks may be transformed, based on the bitmap associated with each data block, into integers value for writing to an integer array or float values for writing to a float array. A tape including a binary representation of the JSON document may be generated based on the bitmap associated with each data block. Related systems and computer program products are also provided.

Abstract: The present disclosure provides a computation device. The computation device is configured to perform a machine learning computation, and includes an operation unit, a controller unit, and a conversion unit. The storage unit is configured to obtain input data and a computation instruction. The controller unit is configured to extract and parse the computation instruction from the storage unit to obtain one or more operation instructions, and to send the one or more operation instructions and the input data to the operation unit. The operation unit is configured to perform operations on the input data according to one or more operation instructions to obtain a computation result of the computation instruction. In the examples of the present disclosure, the input data involved in machine learning computations is represented by fixed-point data, thereby improving the processing speed and efficiency of training operations.

Abstract: Aspects of the invention include physical design-optimal Dadda architectures that scale with increasing operand size. Partial product arrays can be generated for two n-bit operands and columns in the partial product arrays can be shifted to a first row. The number of partial products in each column can be iteratively reduced across one or more stages until each column has at most two partial products. At each stage a maximum column height is determined and each column having a height greater than the maximum column height is reduced using half-adders and full-adders. Result bits of the half-adders and the full-adders are placed at the bottom of the current column and carry bits of the half-adders and the full-adders are placed at the bottom of the next column.

Abstract: One embodiment provides for a graphics processing unit to accelerate machine-learning operations, the graphics processing unit comprising a multiprocessor having a single instruction, multiple thread (SIMT) architecture, the multiprocessor to execute at least one single instruction; and a first compute unit included within the multiprocessor, the at least one single instruction to cause the first compute unit to perform a two-dimensional matrix multiply and accumulate operation, wherein to perform the two-dimensional matrix multiply and accumulate operation includes to compute an intermediate product of 16-bit operands and to compute a 32-bit sum based on the intermediate product.

Abstract: The present disclosure provides a computing method that is applied to a computing device. The computing device includes: a memory, a register unit, and a matrix computing unit. The method includes the following steps: controlling, by the computing device, the matrix computing unit to obtain a first operation instruction, where the first operation instruction includes a matrix reading instruction for a matrix required for executing the instruction; controlling, by the computing device, an operating unit to send a reading command to the memory according to the matrix reading instruction; and controlling, by the computing device, the operating unit to read a matrix corresponding to the matrix reading instruction in a batch reading manner, and executing the first operation instruction on the matrix. The technical solutions in the present disclosure have the advantages of fast computing speed and high efficiency.

Abstract: According to some example embodiments of the present disclosure, in a method for a memory lookup mechanism in a high-bandwidth memory system, the method includes: using a memory die to conduct a multiplication operation using a lookup table (LUT) methodology by accessing a LUT, which includes floating point operation results, stored on the memory die; sending, by the memory die, a result of the multiplication operation to a logic die including a processor and a buffer; and conducting, by the logic die, a matrix multiplication operation using computation units.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for performing mathematical operations on processing units based on data in the modulo space. An example method includes receiving a binary-space input to process (e.g., using a neural network or other processing system). The binary-space input is converted into a modulo-space input based on a set of coprimes defined for executing operations in a modulo space. A modulo-space result is generated through one or more modulo-space multiply-and-accumulate (MAC) units based on the modulo-space input. The modulo-space result is converted into a binary-space result, and the binary-space result is output.

Abstract: Circuitry comprises ray tracing circuitry comprising a plurality of floating-point circuitries to perform floating-point processing operations to detect intersection between a virtual ray defined by a ray direction and a test region, the floating-point circuitries operating to a given precision to generate an output floating-point value comprising a significand and an exponent; in which at least some of the plurality of floating-point circuitries are configured to round using a predetermined directed rounding mode any denormal floating-point value generated by operation of that circuitry so as to output normal values, a denormal floating-point value being a floating-point value in which the significand comprises one or more leading zeroes.

Abstract: A processing-in-memory (PIM) device includes a data selection circuit, a multiplying-and-accumulating (MAC) circuit, and an accumulative adding circuit. The data selection circuit generates selection data from input data and zero-point data based on a zero-point selection signal. The MAC circuit performs a MAC arithmetic operation for the selection data to generate MAC result data. The accumulative adding circuit accumulatively adds MAC sign data based on a MAC output latch signal to generate MAC latch data. A sign of the MAC sign data is determined by the zero-point selection signal.

Abstract: Apparatuses, systems, and techniques to optimize memory usage when performing matrix operations. In at least one embodiment, a matrix is optimized to limit memory and storage requirements while minimizing loss of precision for a sum of the members of the matrix.

Abstract: A disclosed compression method includes inputting a data set of floating point values from an input circuit to a compression circuit and detecting non-zero values and sequences of zero values in the data set. The compression circuit outputs, in response to detection of a non-zero value in the data set, the non-zero value to an output circuit. The compression circuit generates, in response to detection of a sequence of zero values in the data set, a subnormal floating point value having significand bits that indicate counted zero values in the sequence, and outputs the subnormal floating point value to the output circuit.

Abstract: Aspects for neural network operations with floating-point number of short bit length are described herein. The aspects may include a neural network processor configured to process one or more floating-point numbers to generate one or more process results. Further, the aspects may include a floating-point number converter configured to convert the one or more process results in accordance with at least one format of shortened floating-point numbers. The floating-point number converter may include a pruning processor configured to adjust a length of a mantissa field of the process results and an exponent modifier configured to adjust a length of an exponent field of the process results in accordance with the at least one format.

Abstract: The present disclosure provides a computation device. The computation device is configured to perform a machine learning computation, and includes an operation unit, a controller unit, and a conversion unit. The storage unit is configured to obtain input data and a computation instruction. The controller unit is configured to extract and parse the computation instruction from the storage unit to obtain one or more operation instructions, and to send the one or more operation instructions and the input data to the operation unit. The operation unit is configured to perform operations on the input data according to one or more operation instructions to obtain a computation result of the computation instruction. In the examples of the present disclosure, the input data involved in machine learning computations is represented by fixed-point data, thereby improving the processing speed and efficiency of training operations.

Abstract: Systems, apparatuses, and methods related to acceleration circuitry are described. The acceleration circuitry may be deployed in a memory device and can include a memory resource and/or logic circuitry. The acceleration circuitry can perform operations on data to convert the data between one or more numeric formats, such as floating-point and/or universal number (e.g., posit) formats. The acceleration circuitry can perform arithmetic and/or logical operations on the data after the data has been converted to a particular format. For instance, the memory resource can receive data comprising a bit string having a first format that provides a first level of precision. The logic circuitry can receive the data from the memory resource and convert the bit string to a second format that provides a second level of precision that is different from the first level of precision.

Abstract: An apparatus includes a processing circuit and a storage device. The processing circuit is configured to perform one or more processing operations in response to one or more instructions to generate an anchored-data element. The storage device is configured to store the anchored-data element. A format of the anchored-data element includes an identification item, an overlap item, and a data item. The data item is configured to hold a data value of the anchored-data element. The identification item indicates an anchor value for the data value or one or more special values.

Abstract: One embodiment provides for a compute apparatus comprising a decode unit to decode a single instruction into a decoded instruction that specifies multiple operands including a multi-bit input value and a ternary weight associated with a neural network and an arithmetic logic unit including a multiplier, an adder, and an accumulator register. To execute the decoded instruction, the multiplier is to perform a multiplication operation on the multi-bit input based on the ternary weight to generate an intermediate product and the adder is to add the intermediate product to a value stored in the accumulator register and update the value stored in the accumulator register.

Abstract: An example method is provided for encoding text for language processing. The method may be executed by a processing system, and the method includes receiving text comprising a plurality of alphanumeric characters or symbols and converting the text into a numerical vector comprising a plurality of numerical values, by mapping each alphanumeric character or symbol of the text to a vertex coordinate of one of a plurality of vertices of a hypercube, wherein a number of the plurality of vertices is equal to or greater than a number of the plurality of alphanumeric characters or symbols, wherein the numerical vector consumes less space in memory than the text. An amount of time consumed by language processing of the numerical vector may be less than an amount of time consumed by language processing of the text.

Abstract: A universal floating-point Instruction Set Architecture (ISA) compute engine implemented entirely in hardware. The ISA compute engine computes directly with human-readable decimal character sequence floating-point representation operands without first having to explicitly perform a conversion-to-binary-format process in software. A fully pipelined convertToBinaryFromDecimalCharacter hardware operator logic circuit converts one or more human-readable decimal character sequence floating-point representations to IEEE 754-2008 binary floating-point representations every clock cycle. Following computations by at least one hardware floating-point operator, a convertToDecimalCharacterFromBinary hardware conversion circuit converts the result back to a human-readable decimal character sequence floating-point representation.

Abstract: The present disclosure provides a computation device. The computation device is configured to perform a machine learning computation, and includes an operation unit, a controller unit, and a conversion unit. The storage unit is configured to obtain input data and a computation instruction. The controller unit is configured to extract and parse the computation instruction from the storage unit to obtain one or more operation instructions, and to send the one or more operation instructions and the input data to the operation unit. The operation unit is configured to perform operations on the input data according to one or more operation instructions to obtain a computation result of the computation instruction. In the examples of the present disclosure, the input data involved in machine learning computations is represented by fixed-point data, thereby improving the processing speed and efficiency of training operations.

Abstract: Methods, systems, and apparatus for implementing a hexadecimal to quantum computation translation. In one aspect, a method includes obtaining one or more hexadecimal data inputs; applying a quantum computation translation operation to each hexadecimal data input to generate one or more corresponding sequences of quantum computations; implementing the one or more sequences of quantum computations using quantum computing hardware to obtain one or more corresponding sequence of measurement results; and providing the one or more sequences of measurement results as respective representations of the one or more hexadecimal data inputs.

Abstract: Provided is a processor implemented method that includes performing training or an inference operation with a neural network by obtaining a parameter for the neural network in a floating-point format, applying a fractional length of a fixed-point format to the parameter in the floating-point format, performing an operation with an integer arithmetic logic unit (ALU) to determine whether to round off a fixed point based on a most significant bit among bit values to be discarded after a quantization process, and performing an operation of quantizing the parameter in the floating-point format to a parameter in the fixed-point format, based on a result of the operation with the ALU.

Abstract: A method of generating a fixed-point quantized neural network includes analyzing a statistical distribution for each channel of floating-point parameter values of feature maps and a kernel for each channel from data of a pre-trained floating-point neural network, determining a fixed-point expression of each of the parameters for each channel statistically covering a distribution range of the floating-point parameter values based on the statistical distribution for each channel, determining fractional lengths of a bias and a weight for each channel among the parameters of the fixed-point expression for each channel based on a result of performing a convolution operation, and generating a fixed-point quantized neural network in which the bias and the weight for each channel have the determined fractional lengths.

Abstract: A binary logic circuit converts a number in floating point format having an exponent E, an exponent bias B=2ew-1?1, and a significand comprising a mantissa M of mw bits into a fixed point format with an integer width of iw bits and a fractional width of fw bits.

Abstract: A binary logic circuit converts a number in floating point format having an exponent E of ew bits, an exponent bias B given by B=2ew-1?1, and a significand comprising a mantissa M of mw bits into a fixed point format with an integer width of iw bits and a fractional width of fw bits. The circuit includes a shifter operable to receive a significand input comprising a contiguous set of the most significant bits of the significand and configured to left-shift the significand input by a number of bits equal to the value represented by k least significant bits of the exponent to generate a shifter output, wherein min{(ew?1),bitwidth(iw?2?sy)}?k?(ew?1) where sy=1 for a signed floating point number and sy=0 for an unsigned floating point number, and a multiplexer coupled to the shifter and configured to: receive an input comprising a contiguous set of bits of the shifter output; and output the input if the most significant bit of the exponent is equal to one.

Abstract: A processor-implemented includes receiving a first floating point operand and a second floating point operand, each having an n-bit format comprising a sign field, an exponent field, and a significand field, normalizing a binary value obtained by performing arithmetic operations for fields corresponding to each other in the first and second floating point operands for an n-bit multiplication operation, determining whether the normalized binary value is a number that is representable in the n-bit format or an extended normal number that is not representable in the n-bit format, according to a result of the determining, encoding the normalized binary value using an extension bit format in which an extension pin identifying whether the normalized binary value is the extended normal number is added to the n-bit format, and outputting the encoded binary value using the extended bit format, as a result of the n-bit multiplication operation.

Abstract: A data processing system for controlling different types of mobile platforms. The data processing system includes an abstraction component, a standardization component and a driver management. The abstraction component is designed to be connected to one or to multiple platforms, to determine types of mobile platforms, to indicate the types to the driver management and to use drivers provided by the driver management in order to convert messages between an interface to the standardization component and interfaces to the mobile platforms, and/or in order to activate functions of the mobile platforms and of the standardization component. The interfaces of the abstraction component to the mobile platforms include interfaces to the software components of the mobile platforms.

Abstract: An embodiment of the invention is a processor including execution circuitry to, in response to a decoded instruction, convert a half-precision floating-point value to a single-precision floating-point value and store the single-precision floating-point value in each of the plurality of element locations of a destination register. The processor also includes a decoder and the destination register. The decoder is to decode an instruction to generate the decoded instruction.

Abstract: An apparatus has floating-point multiplying circuitry to perform a floating-point multiply operation to multiply first and second floating-point operands to generate a product floating-point value. Shared hardware circuitry of the floating-point multiplying circuitry is reused to also support a floating-point scaling instruction specifying an input floating-point operand and an integer operand, which causes a floating-point scaling operation to be performed to generate an output floating-point value corresponding to a product of the input floating-point operand and a scaling factor 2X, where X is an integer represented by the integer operand.

Abstract: Methods and apparatus for a low energy accelerator processor architecture with short parallel instruction word. An integrated circuit includes a system bus having a data width N, where N is a positive integer; a central processor unit coupled to the system bus and configured to execute instructions retrieved from a memory coupled to the system bus; and a low energy accelerator processor coupled to the system bus and configured to execute instruction words retrieved from a low energy accelerator code memory, the low energy accelerator processor having a plurality of execution units including a load store unit, a load coefficient unit, a multiply unit, and a butterfly/adder ALU unit, each of the execution units configured to perform operations responsive to op-codes decoded from the retrieved instruction words, wherein the width of the instruction words is equal to the data width N. Additional methods and apparatus are disclosed.

Abstract: A computation unit computes a function f(I). The function f(I) has a target output range over a first domain of an input I encoded using a first format. A first circuit receives the encoded input I in the first format including X bits, to add an offset C to the encoded input I to generate an offset input SI=I+C, in a second format including fewer than X bits. The offset C is equal to a difference between the first domain in f(I) and a higher precision domain of the second format for the offset input SI. A second circuit is operatively coupled to receive the offset input SI in the second format, to output a value equal to a function f(SI) to provide an encoded output value f(I).

Abstract: An apparatus includes front-end circuitry to receive radar wave signals and a fast fourier transforms (FFT) signal processor. The FFT signal processor includes multiplication logic circuitry and other logic circuitry. The FFT signal processor derives doppler information from the radar wave signals by operating on a digital stream of input data representing the radar wave signals including using the multiplication logic circuitry to perform multiplication operations on first data in the digital stream while the first data is represented in a signed magnitude form, and using the other logic circuitry to perform other mathematical operations on second data in the digital stream while the second data is represented in a two's complement form.

Abstract: Methods and apparatus relating to conversion of an unsigned normalized (unorm) integer values to floating-point (float) values in low power are described. In an embodiment, conversion logic converts a unorm integer value to a floating-point value based on detection of whether the unorm integer matches one of three cases, wherein the unorm integer value comprises n bits. Memory stores a count value corresponding to n?1 bits of the unorm integer value after detection of a leading 1 in the unorm integer value. The three cases include: a first case with all zeros, a second case with all ones, and a third case with a combination of one or more zeros and one or more ones. Other embodiments are also disclosed and claimed.

Abstract: A floating-point number in a first format representation is received. Based on an identification of a floating-point format type of the floating-point number, different components of the first format representation are identified. The different components of the first format representation are placed in corresponding components of a second format representation of the floating-point number, wherein a total number of bits of the second format representation is larger than a total number of bits of the first format representation. At least one of the components of the second format representation is padded with one or more zero bits. The floating-point number in the second format representation is stored in a register. A multiplication using the second format representation of the floating-point number is performed.

Abstract: Disclosed embodiments relate to systems and methods for performing 16-bit floating-point vector dot product instructions. In one example, a processor includes fetch circuitry to fetch an instruction having fields to specify an opcode and locations of first source, second source, and destination vectors, the opcode to indicate execution circuitry is to multiply N pairs of 16-bit floating-point formatted elements of the specified first and second sources, and accumulate the resulting products with previous contents of a corresponding single-precision element of the specified destination, decode circuitry to decode the fetched instruction, and execution circuitry to respond to the decoded instruction as specified by the opcode.

Abstract: The embodiments herein describe a conversion engine that converts floating point data into integer data using a dynamic scaling factor. To select the scaling factor, the conversion engine compares a default (or initial) scaling factor value to an exponent portion of the floating point value to determine a shift value with which to bit shift a mantissa of the floating point value. After bit shifting the mantissa, the conversion engine determines whether the shift value caused an overflow or an underflow and whether that overflow or underflow violates a predefined policy. If the policy is violated, the conversion engine adjusts the scaling factor and restarts the conversion process. In this manner, the conversion engine can adjust the scaling factor until identifying a scaling factor that converts all the floating point values in the batch without violating the policy.

Abstract: A programmable device may be configured to support machine learning training operations using matrix multiplication circuitry implemented on a systolic array. The systolic array includes an array of processing elements, each of which includes hybrid floating-point dot-product circuitry. The hybrid dot-product circuitry has a hard data path that uses digital signal processing (DSP) blocks operating in floating-point mode and a hard/soft data path that uses DSP blocks operating in fixed-point mode operated in conjunction with general purpose soft logic. The hard/soft data path includes 2-element dot-product circuits that feed an adder tree. Results from the hard data path are combined with the adder tree using format conversion and normalization circuitry. Inputs to the hybrid dot-product circuitry may be in the BFLOAT16 format. The hard data path may be in the single precision format. The hard/soft data path uses a custom format that is similar to but different than BFLOAT16.

Abstract: A method includes dividing a fraction of a floating point result into a first portion and a second portion. The method includes outputting a first normalizer result based on the first portion during to a first clock cycle. The method includes storing a first segment of the first portion during to the first clock cycle. The method includes outputting a first rounder result based on the first normalizer result during to the first clock cycle. The method includes outputting a second normalizer result based on the second portion during to a second clock cycle. The method includes outputting a second rounder result based on the second normalizer result and the first segment during to the second clock cycle.

Abstract: The present disclosure provides a computation device. The computation device is configured to perform a machine learning computation, and includes an operation unit, a controller unit, and a conversion unit. The storage unit is configured to obtain input data and a computation instruction. The controller unit is configured to extract and parse the computation instruction from the storage unit to obtain one or more operation instructions, and to send the one or more operation instructions and the input data to the operation unit. The operation unit is configured to perform operations on the input data according to one or more operation instructions to obtain a computation result of the computation instruction. In the examples of the present disclosure, the input data involved in machine learning computations is represented by fixed-point data, thereby improving the processing speed and efficiency of training operations.

Abstract: Systems, apparatuses, and methods related to acceleration circuitry are described. The acceleration circuitry may be deployed in a memory device and can include a memory resource and/or logic circuitry. The acceleration circuitry can perform operations on data to convert the data between one or more numeric formats, such as floating-point and/or universal number (e.g., posit) formats. The acceleration circuitry can perform arithmetic and/or logical operations on the data after the data has been converted to a particular format. For instance, the memory resource can receive data comprising a bit string having a first format that provides a first level of precision. The logic circuitry can receive the data from the memory resource and convert the bit string to a second format that provides a second level of precision that is different from the first level of precision.

Abstract: Embodiments detailed herein relate to arithmetic operations of float-point values. An exemplary processor includes decoding circuitry to decode an instruction, where the instruction specifies locations of a plurality of operands, values of which being in a floating-point format. The exemplary processor further includes execution circuitry to execute the decoded instruction, where the execution includes to: convert the values for each operand, each value being converted into a plurality of lower precision values, where an exponent is to be stored for each operand; perform arithmetic operations among lower precision values converted from values for the plurality of the operands; and generate a floating-point value by converting a resulting value from the arithmetic operations into the floating-point format and store the floating-point value.

Abstract: Systems and methods for performing a quantization of artificial neural networks (ANNs) are provided. An example method may include receiving a description of an ANN and input data associated with the ANN, wherein the input data are represented according to a first data type; selecting a first value interval of the first data type to be mapped to a second value interval of a second data type; performing, based on the input data and the description of the ANN, the computations of one or more neurons of the ANN, wherein the computations are performed for at least one value within the second value interval, the value being a result of mapping a value of the first value interval to a value of the second value interval; determining, a measure of saturations in neurons of the ANN, and adjusting, based on the measure of saturations, the value intervals.

Abstract: The present application discloses a data processing method and apparatus. A specific implementation of the method includes: receiving floating point data sent from an electronic device; converting the received floating point data into fixed point data according to a data length and a value range of the received floating point data; performing calculation on the obtained fixed point data according to a preset algorithm to obtain result data in a fixed point form; and converting the obtained result data in the fixed point form into result data in a floating point form and sending the result data in the floating point form to the electronic device. This implementation improves the data processing efficiency.

Abstract: An instruction generates a value for use in processing within a computing environment. The instruction obtains a sign control associated with the instruction, and shifts an input value of the instruction in a specified direction by a selected amount to provide a result. The result is placed in a first designated location in a register, and the sign, which is based on the sign control, is placed in a second designated location of the register. The result and the sign provide a signed value to be used in processing within the computing environment.

Abstract: Processing circuitry may support processing of anchor-data values comprising one or more anchored-data elements which represent portions of bits of a two's complement number. The anchored-data processing may depend on anchor information indicating at least one property indicative of a numeric range representable by the result anchored-data element or the anchored-data value. When the operation causes an overflow or an underflow, usage information may be stored indicating a cause of the overflow or underflow and/or an indication of how to update the anchor information and/or number of elements in the anchored-data value to prevent the overflow or underflow. This can support dynamic range adjustment in software algorithms which involve anchored-data processing.

Abstract: Aspects for neural network operations with floating-point number of short bit length are described herein. The aspects may include a neural network processor configured to process one or more floating-point numbers to generate one or more process results. Further, the aspects may include a floating-point number converter configured to convert the one or more process results in accordance with at least one format of shortened floating-point numbers. The floating-point number converter may include a pruning processor configured to adjust a length of a mantissa field of the process results and an exponent modifier configured to adjust a length of an exponent field of the process results in accordance with the at least one format.

Abstract: An arithmetic processor includes a plurality of arithmetic circuits that individually execute an arithmetic operation for fixed point data; and at least one of first and second statistical information is acquired regarding a plurality of fixed point data that are results of arithmetic operation executed by the plurality of arithmetic circuits. The first statistical information is obtained by accumulating a bit pattern, which is obtained by setting a flag bit to each of bit positions corresponding to a range from a least-significant-bit position to a highest-order bit position for each of the digits corresponding to the bit positions, and the second statistical information is obtained by accumulating a bit pattern, which is obtained by setting a flag bit to each of bit positions corresponding to a range from the position of the sign bit to a lowest-order-bit position for each of the digits corresponding to the bit positions.

Abstract: A binary logic circuit converts a number in floating point format having an exponent E, an exponent bias B=2ew-1?1, and a significand comprising a mantissa M of mw bits into a fixed point format with an integer width of iw bits and a fractional width of fw bits.

Abstract: Systems, apparatuses, and methods related to bit string conversion are described. Circuitry can perform operations on bit strings, such as universal number and/or posit bit strings, to alter a level of precision (e.g., a dynamic range, resolution, etc.) of the bit strings. For instance, bit string conversion can include receiving, by a memory resource coupled to logic circuitry, a first bit string having a first bit string length. The first quantity of bits can comprise a first bit sub-set, a second bit sub-set, a third bit sub-set, and a fourth bit sub-set. The logic circuitry monitor numerical values corresponding to at least one bit sub-set of the bit string to determine a dynamic range corresponding to the data and/or precision corresponding to the data and generate a second bit string having a second bit string length based, at least in part, on the determined dynamic range of the data, the precision of the data.