Abstract: Disclosed is a processing unit for computing a convolution of an activations matrix (e.g., a N×N activations matrix) and a weights kernel (e.g., a M×M weights kernel). The processing unit specifically employs an array of processing elements and a hardware-implemented spiral algorithm to compute the convolution. Due to this spiral algorithm, the need for a discrete data setup logic block is avoided, activation values from the activations matrix can be pre-loaded into processing elements only one time so that the need to repeatedly access the activations matrix is avoided, and the computation can be completed in a relatively low number of clock cycles, which is independent of the number of activation values in the activation matrix and which is equal to the number of weight values in a weights kernel. Also disclosed is an associated processing method.

Abstract: A tensor accelerator includes two tile execution units and a bidirectional queue. Each of the tile execution units includes a buffer, a plurality of arithmetic logic units, a network, and a selector. The buffer includes a plurality of memory cells. The network is coupled to the plurality of memory cells. The selector is coupled to the network and the plurality of arithmetic logic units. The bidirectional queue is coupled between the selectors of the tile execution units.

Abstract: An electronic control apparatus includes a first arithmetic processor and a second arithmetic processor that is communicably connected to the first arithmetic processor. The second arithmetic processor includes a controller configured to (i) shift to a rewriting wait state after outputting a request signal that requests a program rewriting to the first arithmetic processor, and (ii) release the rewriting wait state and shift to a program rewriting process after a predetermined wait time that allows the first arithmetic processor to shift to the program rewriting process elapses after outputting the request signal.

Abstract: A method of computing in memory, the method including inputting a packet including data into a computing memory unit having a control unit, loading the data into at least one computing in memory micro-unit, processing the data in the computing in memory micro-unit, and outputting the processed data. Also, a computing in memory system including a computing in memory unit having a control unit, wherein the computing in memory unit is configured to receive a packet having data and a computing in memory micro-unit disposed in the computing in memory unit, the computing in memory micro-unit having at least one of a memory matrix and a logic elements matrix.

Abstract: The disclosure includes a method of authenticating a processor that includes an arithmetic and logic unit. At least one decoded operand of at least a portion of a to-be-executed opcode is received on a first terminal of the arithmetic and logic unit. A signed instruction is received on a second terminal of the arithmetic and logic unit. The signed instruction combines a decoded instruction of the to-be-executed opcode and at least one previously-executed opcode.

Abstract: A cross-host multi-hypervisor system, including a plurality of host sites, each site including at least one hypervisor, each of which includes at least one virtual server, at least one virtual disk that is read from and written to by the at least one virtual server, a tapping driver in communication with the at least one virtual server, which intercepts write requests made by any one of the at least one virtual server to any one of the at least one virtual disk, and a virtual data services appliance, in communication with the tapping driver, which receives the intercepted write requests from the tapping driver, and which provides data services based thereon, and a data services manager for coordinating the virtual data services appliances at the site, and a network for communicatively coupling the plurality of sites, wherein the data services managers coordinate data transfer across the plurality of sites via the network.

Abstract: Execution flows of a program can be characterized by a series of execution events. The rates at which these execution events occur for a particular program can be collected periodically, and the execution events statistics can be utilized for both training a machine learning model, and later on for making classification inferences to determine whether a program run contains any abnormality. When an abnormality is encountered, an alert can be generated and provided to supervisory logic of a computing system to indicate that an abnormal program flow has been detected.

Abstract: Methods and systems for operating internal systems of a vehicle are provided. Aspects include providing a field programmable gate array (FPGA), the FPGA including a communication channel port, wherein the communication channel port is operable to connect to one or more systems through a communication channel, and wherein the FPGA is configured to operate in one or more control modes, receiving a communication channel input to the communication channel port of the FPGA, based at least in part on the communication channel input, determining a control mode from the one or more control modes, and operating the FPGA in the control mode, wherein the control mode is associated with one system of the one or more systems.

Abstract: Apparatuses, systems, and techniques to optimize kernel selection for performing a computation. In at least one embodiment, a neural network is trained and utilized to generate a list of kernels so that an (e.g., optimal) kernel may be identified. The neural network receives characteristics of the input matrices and determines relevancy scores for a list of possible kernels. Based on an ordered listing of kernels by relevant score, a kernel is selected from the list and utilized to perform the computation and provide the result.

Abstract: An integrated circuit device may include programmable logic circuitry on a first integrated circuit die and memory that includes compute-in-memory circuitry on a second die. The programmable logic circuitry may be programmed with a circuit design that operates on a first set of data. The compute-in-memory circuitry of the memory may perform an arithmetic operation using the first set of data from the programmable logic circuitry and a second set of data stored in the memory.

Abstract: A method to compare first and second source data in a processor in response to a vector maximum with indexing instruction includes specifying first and second source registers containing first and second source data, a destination register storing compared data, and a predicate register. Each of the registers includes a plurality of lanes. The method includes executing the instruction by, for each lane in the first and second source register, comparing a value in the lane of the first source register to a value in the corresponding lane of the second source register to identify a maximum value, storing the maximum value in a corresponding lane of the destination register, asserting a corresponding lane of the predicate register if the maximum value is from the first source register, and de-asserting the corresponding lane of the predicate register if the maximum value is from the second source register.

Abstract: Devices and techniques for short-thread rescheduling in a processor are described herein. When an instruction for a thread completes, a result is produced. The condition that the same thread is scheduled in a next execution slot and that the next instruction of the thread will use the result can be detected. In response to this condition, the result can be provided directly to an execution unit for the next instruction.

Abstract: Methods, systems, and devices for multiple concurrent modulation schemes in a memory system are described. Techniques are provided herein to communicate data using a modulation scheme having at least three levels and using a modulation scheme having at least two levels within a common system or memory device. Such communication with multiple modulation schemes may be concurrent. The modulated data may be communicated to a memory die through distinct signal paths that may correspond to a particular modulation scheme. An example of a modulation scheme having at least three levels may be pulse amplitude modulation (PAM) and an example of a modulation scheme having at least two levels may be non-return-to-zero (NRZ).

Abstract: In an example embodiment, a framework to infer a user's value for a particular attribute based upon a multi-task machine learning process with uncertainty weighting that incorporates signals from multiple contexts is provided. In an example embodiment, the framework aims to measure a level of a user attribute under a certain context. Rather than attempting to devise a universal, one-size-fits-all value for the attribute, the framework acknowledges that the user's value for that attribute can vary depending on context and factors in the context under which the user's attribute levels are measured. Multiple contexts are defined depending on different situations where users and entities such as companies and organizations need to evaluate user attribute levels. Signals for attribute levels are then collected for each context. Machine learning models are utilized to estimate attribute values for different contexts. Multi-task deep learning is used to level attributes from different contexts.

Abstract: In a general aspect, hybrid quantum/classical algorithms are executed in a computing system. A first set of values representing a measurement of a reduced density matrix (RDM) is obtained. The first set of values is based on sampling quantum states generated by a quantum processor. A classical processor generates a second, different set of values to represent the measurement of the RDM. The second set of values is constructed based on the first set of values by a process that imposes one or more n-representability conditions on the second set of values to represent the measurement of the RDM.

Abstract: A method can be performed prior to implementation of a neural network by a processing unit. The neural network comprising a succession of layers and at least one operator applied between at least one pair of successive layers. A computational tool generates an executable code intended to be executed by the processing unit in order to implement the neural network. The computational tool generates at least one transfer function between the at least one pair of layers taking the form of a set of pre-computed values.

Abstract: An apparatus to facilitate machine learning matrix processing is disclosed. The apparatus comprises a memory to store matrix data one or more processors to execute an instruction to examine a message descriptor included in the instruction to determine a type of matrix layout manipulation operation that is to be executed, examine a message header included in the instruction having a plurality of parameters that define a two-dimensional (2D) memory surface that is to be retrieved, retrieve one or more blocks of the matrix data from the memory based on the plurality of parameters and a register file including a plurality of registers, wherein the one or more blocks of the matrix data is stored within a first set of the plurality of registers.

Abstract: This disclosure relates to a computing chip, a hashrate board, and a data processing apparatus. The computing chip includes a plurality of operation stages arranged in a pipeline configuration. Each operation stage includes: a first combinational logic circuit occupying a plurality of first cell points adjacent to each other, at least a portion of the first cell points being located in a first incomplete column; one or more second combinational logic circuits each occupying one or more second cell points, at least a portion of the second cell points being located in a second incomplete column; and a plurality of registers each occupying a plurality of third cell points, at least a portion of the third cell points being located in the first incomplete column or the second incomplete column. The first cell points, the second cell points, and third cell points occupy equal areas on the computing chip.

Abstract: Data channel parameter optimization with intelligent selection of initial data channel conditions and optimization algorithm hyperparameters for use of a black box optimizer to optimize one or more data channel parameters. It is currently identified that the initial data channel condition affects the ability of a black box optimizer to optimize data channel parameters. In turn, by use of an intelligent agent (e.g., employing artificial intelligence or machine learning) to iteratively select optimized initial data channel conditions, the optimization of the data channel may be improved. Moreover, the sensitivity of the data channel parameters may be determined, which allows for identification of a subset of data channel parameters that are varied in an optimization approach. This may result in improved performance of the optimization without sacrificing optimized performance of the data channel.

Abstract: The present disclosure relates to a neuron for an artificial neural network. The neuron comprises a dot product engine operative to: receive a set of weights; receive a set of data inputs based on a set of input data signals; and calculate the dot product of the set of data inputs and the set of weights to generate a dot product engine output. The neuron further comprises an activation function module arranged to apply an activation function to a signal indicative of the dot product engine output to generate a neuron output; and gain control circuitry. The gain control circuitry is operative to control: an input gain applied to the input data signals to generate the set of data inputs; and an output gain applied to the dot product engine output or by the activation function module. The output gain is selected to compensate for the applied input gain.