Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can determine allele likelihoods of a genomic region exhibiting certain haplotype alleles using one or both of consolidated computations and data exchanges across specialized hardware. For instance, the disclosed systems can determine an intermediate allele likelihood of a genomic region comprising a haplotype allele by running a single-pass-concurrent-multiplication operation. In some cases, the disclosed systems determine and store subsets of intermediate allele likelihoods corresponding to marker-variant groups and extemporaneously generate sets of intermediate allele likelihoods for a set of marker variants by using the intermediate-allele-likelihood subsets as hot-start points.

Abstract: A network device may receive traffic to be processed by a routing component, and may determine temperatures of an ASIC and an HBM of the routing component at a first time. The network device may determine whether the temperature of the ASIC satisfies a first ASIC temperature threshold or a second ASIC temperature threshold, and may determine whether the temperature of the HBM satisfies a first HBM temperature threshold or a second HBM temperature threshold. The network device may selectively throttle processing of the traffic by a first quantity when the temperature of the ASIC satisfies the first ASIC temperature threshold or the temperature of the HBM satisfies the first HBM temperature threshold, or throttle the processing of the traffic by a second quantity when the temperature of the ASIC satisfies the second ASIC temperature threshold or the temperature of the HBM satisfies the second HBM temperature threshold.

Abstract: A method of base calling using at least two base callers is disclosed. The method includes executing at least a first base caller and a second base caller on sensor data generated for sensing cycles in a series of sensing cycles; generating, by the first base caller, first classification information associated with the sensor data, based on executing the first base caller on the sensor data; and generating, by the second base caller, second classification information associated with the sensor data, based on executing the second base caller on the sensor data. In an example, based on the first classification information and the second classification information, a final classification information is generated, where the final classification information includes one or more base calls for the sensor data.

Abstract: Methods, systems, and non-transitory computer readable media are disclosed for accurately and efficiently detect when bubbles impact nucleic-acid-sequencing runs based on data captured during (or derived from) base calls during sequencing runs. In particular, in one or more embodiments, the disclosed systems receive data identifying nucleobase calls and data identifying quality metrics for the nucleobase calls during sequencing cycles. Based on particular nucleobase calls and threshold markers for the quality metrics, the disclosed system utilizes a machine-learning-model to detect a presence of a bubble in a nucleotide-sample slide. Beyond simply detecting the presence of a bubble, the disclosed system can also classify different detected bubbles, such as air bubbles, oil bubbles, or ghost bubbles, or other outputs during sequencing.

Abstract: A system for base calling includes memory storing a topology of a neural network, a plurality of weights sets, and sensor data for a series of sensing cycles. Sequencing events span temporal progression of the base calling operation through subseries of sensing cycles, and spatial progression of the base calling operation through locations on a biosensor. A configurable processor is configured to load the topology on the configurable processor, select a weight set in dependence upon a subject subseries of sensing cycles and/or a subject location on the biosensor, load subject sensor data for the subject subseries of sensing cycles and the subject location on the processing elements, configure the topology using the selected weight set, and cause the neural network to process the subject sensor data to produce base call classification data for the subject subseries and the subject location.

Abstract: A network device may receive traffic to be processed by a routing component, and may determine temperatures of an ASIC and an HBM of the routing component at a first time. The network device may determine whether the temperature of the ASIC satisfies a first ASIC temperature threshold or a second ASIC temperature threshold, and may determine whether the temperature of the HBM satisfies a first HBM temperature threshold or a second HBM temperature threshold. The network device may selectively throttle processing of the traffic by a first quantity when the temperature of the ASIC satisfies the first ASIC temperature threshold or the temperature of the HBM satisfies the first HBM temperature threshold, or throttle the processing of the traffic by a second quantity when the temperature of the ASIC satisfies the second ASIC temperature threshold or the temperature of the HBM satisfies the second HBM temperature threshold.

Abstract: A network device may receive traffic to be processed by a routing component, and may determine temperatures of an ASIC and an HBM of the routing component at a first time. The network device may determine whether the temperature of the ASIC satisfies a first ASIC temperature threshold or a second ASIC temperature threshold, and may determine whether the temperature of the HBM satisfies a first HBM temperature threshold or a second HBM temperature threshold. The network device may selectively throttle processing of the traffic by a first quantity when the temperature of the ASIC satisfies the first ASIC temperature threshold or the temperature of the HBM satisfies the first HBM temperature threshold, or throttle the processing of the traffic by a second quantity when the temperature of the ASIC satisfies the second ASIC temperature threshold or the temperature of the HBM satisfies the second HBM temperature threshold.

Abstract: A system for analysis of base call sensor output has memory accessible by the runtime program storing tile data including sensor data for a tile from sensing cycles of a base calling operation. A neural network processor having access to the memory is configured to execute runs of a neural network using trained parameters to produce classification data for sensing cycles. A run of the neural network operates on a sequence of N arrays of tile data from respective sensing cycles of N sensing cycles, including a subject cycle, to produce the classification data for the subject cycle. Data flow logic moves tile data and the trained parameters from the memory to the neural network processor for runs of the neural network using input units including data for spatially aligned patches of the N arrays from respective sensing cycles of N sensing cycles.

Abstract: Systems, methods, and computer programs for analyzing genetic sequence data is disclosed. In one aspect, the system can include one or more of a first integrated circuit, with each first integrated circuit forming a central processing unit (CPU) that is responsive to one or more software algorithms that are configured to instruct the CPU to perform a first set of genomic processing steps of a sequence analysis pipeline. Additionally, the system can include one or more second integrated circuits, with each second integrated circuit forming a field programmable gate array (FPGA). The FPGA can be configured by firmware to arrange a set of hardwired digital logic circuits to perform a second set of genomic processing stages of the sequence analysis pipeline, the set of hardwired digital logic circuits of each FPGA being arranged as a set of processing engines to perform the second set of genomic processing stages.

Abstract: A system, method and apparatus for executing a bioinformatics analysis on genetic sequence data is provided. Particularly, a genomics analysis platform for executing a sequence analysis pipeline is provided. The genomics analysis platform includes one or more of a first integrated circuit, where each first integrated circuit forms a central processing unit (CPU) that is responsive to one or more software algorithms that are configured to instruct the CPU to perform a first set of genomic processing steps of the sequence analysis pipeline.

Abstract: A system, method and apparatus for executing a bioinformatics analysis on genetic sequence data is provided. Particularly, a genomics analysis platform for executing a sequence analysis pipeline is provided. The genomics analysis platform includes one or more of a first integrated circuit, where each first integrated circuit forms a central processing unit (CPU) that is responsive to one or more software algorithms that are configured to instruct the CPU to perform a first set of genomic processing steps of the sequence analysis pipeline.

Abstract: A system, method and apparatus for executing a bioinformatics analysis on genetic sequence data is provided. Particularly, a genomics analysis platform for executing a sequence analysis pipeline is provided. The genomics analysis platform includes one or more of a first integrated circuit, where each first integrated circuit forms a central processing unit (CPU) that is responsive to one or more software algorithms that are configured to instruct the CPU to perform a first set of genomic processing steps of the sequence analysis pipeline.

Abstract: A system, method and apparatus for executing a bioinformatics analysis on genetic sequence data is provided. Particularly, a genomics analysis platform for executing a sequence analysis pipeline is provided. The genomics analysis platform includes one or more of a first integrated circuit, where each first integrated circuit forms a central processing unit (CPU) that is responsive to one or more software algorithms that are configured to instruct the CPU to perform a first set of genomic processing steps of the sequence analysis pipeline.

Abstract: A baseband processing module according to the present invention includes a multi-path scanner module. The multi-path scanner module is operable to receive timing and scrambling code information regarding an expected multi-path signal component of a WCDMA signal. Then, the multi-path scanner module is operable to identify a plurality of multi-path signal components of the WCDMA signal by descrambling, despreading and correlating a known symbol pattern of/with a baseband RX signal within a search window. The multi-path scanner module is operable to determine timing information for the plurality of multi-path signal components of the WCDMA signal found within the search window and to pass this information to a coupled rake receiver combiner module.

Abstract: A network device component receives traffic, determines whether the traffic is host bound traffic or non-host bound traffic, and classifies, based on a user-defined classification scheme, the traffic when the traffic is host bound traffic. The network device component also assigns, based on the classification, the classified host bound traffic to a queue associated with network device component for forwarding the classified host bound traffic to a host component of the network device.

Abstract: A baseband processing module includes an RX interface, a rake receiver combiner module, and may include additional components. The RX interface receives the baseband signals from an RF front end and creates baseband RX signal samples there from. The rake receiver combiner module includes control logic, an input buffer, a rake despreader module, and an output buffer. The rake despreader module is operable to despread the baseband RX signal samples in a time divided fashion to produce channel symbols including pilot channel symbols and physical channel symbols.

Abstract: A baseband processing module for use within a Radio Frequency (RF) transceiver includes a downlink/uplink interface, TX processing components, a processor, memory, RX processing components, and a turbo decoding module. The RX processing components receive a baseband RX signal from the RF front end, produce a set of IR samples from the baseband RX signal, and transfer the set of IR samples to the memory. The turbo decoding module receives a set of IR samples from the memory, forms a turbo code word from the set of IR samples, turbo decodes the turbo code word to produce inbound data, and outputs the inbound data to the downlink/uplink interface. The turbo decoding module performs metric normalization based upon a chosen metric, performs de-rate matching on the set of IR samples, performs error detection operations, and extracts information from a MAC packet that it produces.

Abstract: A baseband processing module for use within a Radio Frequency (RF) transceiver includes a downlink/uplink interface, TX processing components, a processor, memory, RX processing components, and a turbo decoding module. The RX processing components receive a baseband RX signal from the RF front end, produce a set of IR samples from the baseband RX signal, and transfer the set of IR samples to the memory. The turbo decoding module receives at least one set of IR samples from the memory, forms a turbo code word from the at least one set of IR samples, turbo decodes the turbo code word to produce inbound data, and outputs the inbound data to the downlink/uplink interface. The turbo decoding module performs metric normalization based upon a chosen metric performs error detection operations, and extracts information from a MAC packet that it produces.

Abstract: A baseband processing module for use within a Radio Frequency (RF) transceiver includes a downlink/uplink interface, TX processing components, a processor, memory, RX processing components, and a turbo decoding module. The RX processing components receive a baseband RX signal from the RF front end, produce a set of IR samples from the baseband RX signal, and transfer the set of IR samples to the memory. The turbo decoding module receives at least one set of IR samples from the memory, forms a turbo code word from the at least one set of IR samples, turbo decodes the turbo code word to produce inbound data, and outputs the inbound data to the downlink/uplink interface. The turbo decoding module performs metric normalization based upon a chosen metric, performs de-rate matching, performs error detection operations, and extracts information from a MAC packet that it produces.

Abstract: A network device component receives traffic, determines whether the traffic is host bound traffic or non-host bound traffic, and classifies, based on a user-defined classification scheme, the traffic when the traffic is host bound traffic. The network device component also assigns, based on the classification, the classified host bound traffic to a queue associated with network device component for forwarding the classified host bound traffic to a host component of the network device.