Abstract: To enables to obtain difficult-to-predict random numbers without requiring a more complex circuit configuration, the random number generating circuit is configured to include a light output means, a light receiving means, a current/voltage converting means, a comparing means, a sampling means, and an output means. The light output means outputs light of a prescribed wavelength. The light receiving means receives the light and converts the same into current signals. The current/voltage converting means converts the current signals into voltage signals. The comparing means compares the voltage signals with a reference voltage and converts the voltage signals into binary signals. The sampling means samples, at a cycle set on the basis of the light noise frequency, the binary signals converted by the comparing means and converts the same into bit data. The output means outputs bit string data arranged in the order of conversion of the bit data.

Abstract: An optimization apparatus includes: a memory; and a processor coupled to the memory, the processor being configured to (a): execute transformation processing that includes transforming an input first cost function to a second cost function by using at least any one of “Legendre transformation,” “Lagrange function,” and “Wolfe's duality theorem,” (b): execute binary expansion processing that includes acquiring values in an Ising format by carrying out binary expansion for values relating to coefficients of the second cost function, (c): execute Ising machine processing that includes seeking values that represent a ground state regarding the values in the Ising format relating to the coefficients of the second cost function, and (d): execute reverse binary expansion processing that includes acquiring values that minimize the first cost function by carrying out reverse binary expansion for the sought values that represent the ground state.

Abstract: The present disclosure relates to an electronic system for computing items of an outer product matrix, for each item of at least part of the items of the matrix. The system is configured to receive a pair of real numbers of two vectors, the pair corresponding to the item. The system is further configured to compute a stochastic representation of the real numbers resulting in two sets of bits, the set of bits comprising a subset of bits representing the real number and a sign bit indicative of the sign of the real number. The system is further configured to perform a sequence of digital operations using the two sets of bits to provide a representation of the item.

Abstract: A function estimation hardware logic unit may be implemented as part of an execution pipeline in a processor. The function estimation hardware logic unit is arranged to calculate, in hardware logic, an improved estimate of a function of an input value, d, where the function is given by 1 ? / ? d i . The hardware logic comprises a plurality of multipliers and adders arranged to implement a mth-order polynomial with coefficients that are rational numbers, where m is not equal to two and in various examples m is not equal to a power of two. In various examples i=1, i=2 or i=3. In various examples m=3.

Abstract: Systems and methods are provided to enable parallelized multiply-accumulate operations in a systolic array. Each row of the systolic array can include multiple busses enabling independent transmission of inputs along the respective bus. Each processing element can include a plurality of interconnects to receive a plurality of inputs corresponding to the multiple busses. Each processing element of a given row-oriented bus can receive an input from a prior element of the given row-oriented bus at an active bus position and perform arithmetic operations on the input. Each processing element can further receive a plurality of inputs at passive bus positions and provide the plurality of inputs to subsequent processing elements without the plurality of inputs being processed by the processing element. Use of row-oriented busses can enable parallelization to increase speed or enable increased latency at individual processing elements.

Abstract: A computer-implemented optimization problem arithmetic method includes determining, based on management information indicating a partition mode that defines a logically divided state of each of a plurality of arithmetic circuits and utilization information relating to each of the plurality of arithmetic circuits, a partition mode of each of the plurality of arithmetic circuits, receiving a combinatorial optimization problem, selecting, based on information relating to scale or requested accuracy of the combinatorial optimization problem and the determined partition mode of each of the plurality of arithmetic units, a first arithmetic circuit from among the plurality of arithmetic circuits, and causing the selected first arithmetic circuit to execute arithmetic operation of the combinatorial optimization problem based on a first partition mode determined as the partition mode of the first arithmetic circuit.

Abstract: Apparatus and associated methods relate to determining a natural exponent from a digital word input by splitting the digital word, and retrieving a precalculated and predetermined value from a data store at an address defined by the first word. In an illustrative example, the retrieved value may be a hyperbolic sum. The hyperbolic sum may be multiplied by the second word. The hyperbolic sum may be scaled, and summed with the multiplication result to generate a scaled exponential value. The scaled exponential value may be scaled to produce an exponential value representing eX. In various examples, the digital word input may be in a fixed point or a floating point format, or converted therebetween. In various embodiments, the data store may be a lookup table. Various examples may provide a compact and versatile architecture for determining a natural exponent with minimized hardware resources.

Abstract: An analog vector-matrix multiplication circuit is achieved by using a programmable storage device array. In a programmable semiconductor device array, gates of all of programmable semiconductor devices of each row are all connected to the same analog voltage input end. M rows of programmable semiconductor devices are correspondingly connected to M analog voltage input ends. Drains (or sources) of all of programmable semiconductor devices of each column are all connected to the same bias voltage input end. N columns of programmable semiconductor devices are correspondingly connected to N bias voltage input ends. Sources (or drains) of all of programmable semiconductor devices of each column are all connected to the same analog current output end. The N columns of programmable semiconductor devices are correspondingly connected to N analog current output ends.

Abstract: Certain aspects provide methods and apparatus for binary computation. An example circuit for such computation generally includes a memory cell having at least one of a bit-line or a complementary bit-line; a computation circuit coupled to a computation input node of the circuit and the bit-line or the complementary bit-line; and an adder coupled to the computation circuit, wherein the computation circuit comprises a first n-type metal-oxide-semiconductor (NMOS) transistor coupled to the memory cell, and a first p-type metal-oxide-semiconductor (PMOS) transistor coupled to the memory cell, drains of the first NMOS and PMOS transistors being coupled to the adder, wherein a source of the first PMOS transistor is coupled to a reference potential node, and wherein a source of the first NMOS transistor is coupled to the computation input node.

Abstract: An information processing apparatus includes: a memory; and a processor coupled to the memory and configured to: hold values of a plurality of state variables included in an evaluation function representing energy, and outputs, every certain number of trials, the values of the plurality of state variables; compute, when a state transition occurs in response to changing of one of the values of the plurality of state variables, an energy change value for each state transition based on a weight value selected based on an update index value; and determine a first offset value based on a plurality of the energy change values such that at least one of the state transitions is allowed, outputs a plurality of first evaluation values obtained by adding the first offset value to the plurality of energy change values, and outputs, every certain number of trials, the first offset value.

Abstract: Methods and apparatus for approximation using polynomial functions are disclosed. In one embodiment, a processor comprises decoding and execution circuitry. The decoding circuitry is to decode an instruction, where the instruction comprises a first operand specifying an output location and a second operand specifying a plurality of data element values to be computed. The execution circuitry is to execute the decoded instruction. The execution includes to compute a result for each of the plurality of data element values using a polynomial function to approximate a complex function, where the computation uses coefficients stored in a lookup location for the complex function, and where data element values within different data element value ranges use different sets of coefficients. The execution further includes to store results of the computation in the output location.

Abstract: A function approximation system is disclosed for determining output floating point values of functions calculated using floating point numbers. Complex functions have different shapes in different subsets of their input domain, making them difficult to predict for different values of the input variable. The function approximation system comprises an execution unit configured to determine corresponding values of a given function given a floating point input to the function; a plurality of look up tables for each function type; a correction table of values which determines if corrections to the output value are required; and a table selector for finding an appropriate table for a given function.

Abstract: A memory device is provided. The memory device includes: a memory cell configured to store weight data, a buffer memory configured to read the weight data from the memory cell, an input/output pad configured to receive input data and a multiply-accumulate (MAC) operator configured to receive the weight data from the buffer memory and receive the input data from the input/output pad to perform a convolution operation of the weight data and the input data, wherein the input data is provided to the MAC operator during a first period, and wherein the MAC operator performs the convolution operation of the weight data and the input data during a second period overlapping with the first period.

Abstract: A differential mixed-signal logic processor is provided. The differential mixed-signal logic processor includes a plurality of mixed-signal multiplier branches for multiplication of an analog value A and a N-bit digital value B. Each of the plurality of mixed-signal multiplier branches include a first capacitor connected across a second capacitor and a third capacitor to provide a differential output across the second and third capacitors. A capacitance of the first capacitor is equal to half a capacitance of the second and third capacitors.

Abstract: Systems, apparatuses and methods may provide for technology that conduct a first alignment between a plurality of floating-point numbers based on a first subset of exponent bits. The technology may also conduct, at least partially in parallel with the first alignment, a second alignment between the plurality of floating-point numbers based on a second subset of exponent bits, where the first subset of exponent bits are LSBs and the second subset of exponent bits are MSBs. In one example, technology adds the aligned plurality of floating-point numbers to one another. With regard to the second alignment, the technology may also identify individual exponents of a plurality of floating-point numbers, identify a maximum exponent across the individual exponents, and conduct a subtraction of the individual exponents from the maximum exponent, where the subtraction is conducted from MSB to LSB.

Abstract: A data processing apparatus is provided to convert a plurality of signed digits to an output value, the data processing apparatus comprising: receiver circuitry to receive, at each of a plurality of iterations, a signed digit from the plurality of signed digits, and previous intermediate data. Conversion circuitry performs a negative-output conversion from the signed digit to an unsigned digit, such that the output value comprising the unsigned digit is negative. Concatenation circuitry concatenate bits of the unsigned digit and bits of the previous intermediate data to produce updated intermediate data and output circuitry provides the updated intermediate data as the previous intermediate data of a next iteration. After the plurality of iterations, the output circuitry outputs at least part of the updated intermediate data as the output value.

Abstract: As part of a signal processing event, the maximum frequency of an input signal can be determined with a processor. The maximum frequency can be compared to a value generated with a decimator/interpolator. Based on the comparison, the sampling rate for sampling the input signal with the processor can be set as part of the digital signal processing event. The sampling rate can be adjusted as the frequency of the input signal varies during the signal processing event.

Abstract: Disclosed embodiments relate to an interleaved pipeline of floating-point (FP) adders. In one example, a processor is to execute an instruction specifying an opcode and locations of a M by K first source matrix, a K by N second source matrix, and a M by N destination matrix, the opcode indicating execution circuitry, for each FP element (M, N) of the destination matrix, is to: launch K instances of a pipeline having a first, MULTIPLY stage, during which a FP element (M, K) of the first source matrix and a corresponding FP element (K, N) of the second source matrix are multiplied; concurrently, in an EXPDIFF stage, determine an exponent difference between the product and a previous FP value of the element (M, N) of the destination matrix; and in a second, ADD-BYPASS stage, accumulate the product with the previous FP value and, concurrently, bypassing the accumulated sum to a subsequent pipeline instance.

Abstract: A matrix processing apparatus having a three-dimensional slice access memory and an input-/output block. The slice access memory includes cells organized into cell slices, each slice storing an entire selected data matrix. The three-dimensional slice access memory is configured to allow read/write access to the entire data matrix at the same time. The input/output block is connected to the three-dimensional slice access memory and is configured to format data into a format acceptable to the three-dimensional slice access memory.

Abstract: A device configured to emulate a quadrilateral lattice correlithm object generator includes multiple processing stages that operate together to output a quadrilateral lattice correlithm object. A quadrilateral lattice correlithm object has a quadrilateral geometric shape and is formed by at least four sub-lattice correlithm objects that are some number of bits away from each other in n-dimensional space.