Abstract: A login system allows users to access computer systems without using a password. The passwordless system and method can use other information to securely and reliably identify true authorized system users. The identity of a user can be associated with their mobile device. The login can be based upon a minimal amount of information such as a name and a phone number which can be stored as an identification record for each of the users in a database.

Abstract: The present invention relates to methods, devices and systems for associating consumable data with an assay consumable used in a biological assay. Provided are assay systems and associated consumables, wherein the assay system adjusts one or more steps of an assay protocol based on consumable data specific for that consumable. Various types of consumable data are described, as well as methods of using such data in the conduct of an assay by an assay system. The present invention also relates to consumables (e.g., kits and reagent containers), software, data deployable bundles, computer-readable media, loading carts, instruments, systems, and methods, for performing automated biological assays.

Abstract: Instruction generation for a data processing array and microcontroller includes generating a tensor-level intermediate representation from a machine learning model using kernel expressions. Statements of the tensor-level intermediate representation are partitioned into a first set of statements and a second set of statements. From the first set of statements, kernel instructions are generated based on a reconfigurable neural engine model. The kernel instructions are executable by a compute tile of a data processing array to implement compute functions of the machine learning model. From the set of second statements, microcontroller instructions are generated based on a super-graph model. The microcontroller instructions are executable by a microcontroller of the data processing array to move data into and out from the data processing array.

Abstract: A web based finance system allows users to refinance past eligible purchasing transactions. The system can include a system server(s) which is in communication through a network with client computing devices associated with customers. The server can receive customer information and determine a refinancing value range based upon the user has a bank account and/or credit risk rating information. The server can identify prior eligible credit card purchases for refinancing and the customer can select one or more purchases for refinancing and the refinancing loan terms.

Abstract: Controlling a data processing (DP) array includes creating a replica of a register address space of the DP array based on the design and the DP array. A sequence of instructions, including write instructions and read instructions, is received. The write instructions correspond to buffer descriptors specifying runtime data movements for a design for a DP array. The write instructions are converted into transaction instructions and the read instructions are converted into wait instructions based on the replica of the register address space. The transaction instructions and the wait instructions are included in an instruction buffer. The instruction buffer is provided to a microcontroller configured to execute the transaction instructions and the wait instructions to implement the runtime data movements for the design as implemented in the DP array. In another aspect, the instruction buffer is stored in a file for subsequent execution by the microcontroller.

Abstract: An integrated circuit includes a plurality of kernels and a virtual machine coupled to the plurality of kernels. The virtual machine is configured to interpret instructions directed to different ones of the plurality of kernels. The virtual machine is configured to control operation of the different ones of the plurality of kernels responsive to the instructions.

Abstract: A login system allows users to access computer systems without using a password. The passwordless system and method can use other information to securely and reliably identify true authorized system users. The identity of a user can be associated with their mobile device. The login can be based upon a minimal amount of information such as a name and a phone number which can be stored as an identification record for each of the users in a database.

Abstract: A programmable, non-linear (PNL) activation engine for a neural network is capable of receiving input data within a circuit. In response to receiving an instruction corresponding to the input data, the PNL activation engine is capable of selecting a first non-linear activation function from a plurality of non-linear activation functions by decoding the instruction. The PNL activation engine is capable of fetching a first set of coefficients corresponding to the first non-linear activation function from a memory. The PNL activation engine is capable of performing a polynomial approximation of the first non-linear activation function on the input data using the first set of coefficients. The PNL activation engine is capable of outputting a result from the polynomial approximation of the first non-linear activation function.

Abstract: Financing tools can provide a flexible credit services to customer. A credit service provider can collect personal data from clients that can include a mobile telephone number and a legal name of the client as well as purchase information from a merchant. Based upon the collected data, the system can determine a client credit risk. The system can make a credit decision to offer a client credit to purchase goods or services based upon the credit risk.

Abstract: A web based finance system allows users to refinance past eligible purchasing transactions. The system can include a system server(s) which is in communication through a network with client computing devices associated with customers. The server can receive customer information and determine a refinancing value range based upon the user has a bank account and/or credit risk rating information. The server can identify prior eligible credit card purchases for refinancing and the customer can select one or more purchases for refinancing and the refinancing loan terms.

Abstract: An inference server is capable of receiving a plurality of inference requests from one or more client systems. Each inference request specifies one of a plurality of different endpoints. The inference server can generate a plurality of batches each including one or more of the plurality of inference requests directed to a same endpoint. The inference server also can process the plurality of batches using a plurality of workers executing in an execution layer therein. Each batch is processed by a worker of the plurality of workers indicated by the endpoint of the batch.

Abstract: Financing tools can provide a flexible credit services to customer. A credit service provider can collect personal data from clients that can include a mobile telephone number and a legal name of the client as well as purchase information from a merchant. Based upon the collected data, the system can determine a client credit risk. The system can make a credit decision to offer a client credit to purchase goods or services based upon the credit risk.

Abstract: Embodiments herein describe techniques for interfacing a neural network application with a neural network accelerator using a library. The neural network application may execute on a host computing system while the neural network accelerator executes on a massively parallel hardware system, e.g., a FPGA. The library operates a pipeline for submitting the tasks received from the neural network application to the neural network accelerator. In one embodiment, the pipeline includes a pre-processing stage, an FPGA execution stage, and a post-processing stage which each correspond to different threads. When receiving a task from the neural network application, the library generates a packet that includes the information required for the different stages in the pipeline to perform the tasks. Because the stages correspond to different threads, the library can process multiple packets in parallel which can increase the utilization of the neural network accelerator on the hardware system.

Abstract: In the disclosed methods and systems for processing in a neural network system, a host computer system writes a plurality of weight matrices associated with a plurality of layers of a neural network to a memory shared with a neural network accelerator. The host computer system further assembles a plurality of per-layer instructions into an instruction package. Each per-layer instruction specifies processing of a respective layer of the plurality of layers of the neural network, and respective offsets of weight matrices in a shared memory. The host computer system writes input data and the instruction package to the shared memory. The neural network accelerator reads the instruction package from the shared memory and processes the plurality of per-layer instructions of the instruction package.

Abstract: A disclosed neural network processing system includes a host computer system, a RAMs coupled to the host computer system, and neural network accelerators coupled to the RAMs, respectively. The host computer system is configured with software that when executed causes the host computer system to write input data and work requests to the RAMS. Each work request specifies a subset of neural network operations to perform and memory locations in a RAM of the input data and parameters. A graph of dependencies among neural network operations is built and additional dependencies added. The operations are partitioned into coarse grain tasks and fine grain subtasks for optimal scheduling for parallel execution. The subtasks are scheduled to accelerator kernels of matching capabilities. Each neural network accelerator is configured to read a work request from the respective RAM and perform the subset of neural network operations on the input data using the parameters.

Abstract: A web based finance system allows users to refinance past eligible purchasing transactions. The system can include a system server(s) which is in communication through a network with client computing devices associated with customers. The server can receive customer information and determine a refinancing value range based upon the user has a bank account and/or credit risk rating information. The server can identify prior eligible credit card purchases for refinancing and the customer can select one or more purchases for refinancing and the refinancing loan terms.

Abstract: A web based finance system allows users to refinance past eligible purchasing transactions. The system can include a system server(s) which is in communication through a network with client computing devices associated with customers. The server can receive customer information and determine a refinancing value range based upon the user has a bank account and/or credit risk rating information. The server can identify prior eligible credit card purchases for refinancing and the customer can select one or more purchases for refinancing and the refinancing loan terms.

Abstract: A login system allows users to access computer systems without using a password. The passwordless system and method can use other information to securely and reliably identify true authorized system users. The identity of a user can be associated with their mobile device. The login can be based upon a minimal amount of information such as a name and a phone number which can be stored as an identification record for each of the users in a database.

Abstract: In disclosed approaches of neural network processing, a host computer system copies an input data matrix from host memory to a shared memory for performing neural network operations of a first layer of a neural network by a neural network accelerator. The host instructs the neural network accelerator to perform neural network operations of each layer of the neural network beginning with the input data matrix. The neural network accelerator performs neural network operations of each layer in response to the instruction from the host. The host waits until the neural network accelerator signals completion of performing neural network operations of layer i before instructing the neural network accelerator to commence performing neural network operations of layer i+1, for i?1. The host instructs the neural network accelerator to use a results data matrix in the shared memory from layer i as an input data matrix for layer i+1 for i?1.

Abstract: An example preprocessor circuit includes: a first buffer configured to store rows of image data and output a row thereof; a second buffer, coupled to the first buffer, including storage locations to store respective image samples of the row output by the first buffer; shift registers; an interconnect network including connections, each connection coupling a respective one of the shift registers to more than one of the storage locations, one or more of the storage locations being coupled to more than one of the connections; and a control circuit configured to load the shift registers with the image samples based on the connections and shift the shift registers to output streams of image samples.