A test and measurement system has a test and measurement instrument, a test automation platform, and one or more processors, the one or more processors configured to execute code that causes the one or more processors to receive a waveform created by operation of a device under test, generate one or more tensor arrays, apply machine learning to a first tensor array of the one or more tensor arrays to produce equalizer tap values, apply machine learning to a second tensor array of the one of the one or more tensor arrays to produce predicted tuning parameters for the device under test, use the equalizer tap values to produce a Transmitter and Dispersion Eye Closure Quaternary (TDECQ) value, and provide the TDECQ value and the predicted tuning parameters to the test automation platform. A method of testing devices under test includes receiving a waveform created by operation of a device under test, generating one or more tensor arrays, applying machine learning to a first tensor array of the one or more tensor arrays to produce equalizer tap values, applying machine learning to a second tensor array of the one or more tensor arrays to produce predicted tuning parameters for the device under test, using the equalizer tap values to produce a Transmitter Dispersion Eye Closure Quaternary (TDECQ) value, and providing the TDECQ value and the predicted tuning parameters to a test automation platform.

A test and measurement system has a test and measurement instrument, a test automation platform, and one or more processors, the one or more processors configured to execute code that causes the one or more processors to receive a waveform created by operation of a device under test, generate one or more tensor arrays, apply machine learning to a first tensor array of the one or more tensor arrays to produce equalizer tap values, apply machine learning to a second tensor array of the one of the one or more tensor arrays to produce predicted tuning parameters for the device under test, use the equalizer tap values to produce a Transmitter and Dispersion Eye Closure Quaternary (TDECQ) value, and provide the TDECQ value and the predicted tuning parameters to the test automation platform. A method of testing devices under test includes receiving a waveform created by operation of a device under test, generating one or more tensor arrays, applying machine learning to a first tensor array of the one or more tensor arrays to produce equalizer tap values, applying machine learning to a second tensor array of the one or more tensor arrays to produce predicted tuning parameters for the device under test, using the equalizer tap values to produce a Transmitter Dispersion Eye Closure Quaternary (TDECQ) value, and providing the TDECQ value and the predicted tuning parameters to a test automation platform.

Techniques and apparatus for remotely accessing debugging resources of a target system are described. A target system including physical compute resources, such as, processors and a chipset can be coupled to a controller remotely accessible over a network. The controller can be arranged to facilitate remote access to debug resources of the physical compute resources. The controller can be coupled to debug pin, such as, those of a debug port and arranged to assert control signals on the pins to access debug resources. The controller can also be arranged to exchange information elements with a remote debug host to include indication of debug operations and/or debug results.

Techniques and apparatus for remotely accessing debugging resources of a target system are described. A target system including physical compute resources, such as, processors and a chipset can be coupled to a controller remotely accessible over a network. The controller can be arranged to facilitate remote access to debug resources of the physical compute resources. The controller can be coupled to debug pin, such as, those of a debug port and arranged to assert control signals on the pins to access debug resources. The controller can also be arranged to exchange information elements with a remote debug host to include indication of debug operations and/or debug results.

Topology discovery of a target system having a plurality of components coupled with a scan topology may be performed by driving a low logic value on the data input signal and a data output signal of the scan topology. An input data value and an output data value for each of the plurality of components is sampled and recorded. A low logic value is then scanned through the scan path and recorded at each component. The scan topology may be determined based on the recorded data values and the recorded scan values.

An integrated circuit (IC) includes first and scan latches that are enabled to load data during a first part of a clock period. A clocking circuit outputs latch clocks with one latch clock driven to an active state during a second part of the clock period dependent on a first address input. A set of storage elements have inputs coupled to the output of the first scan latch and are respectively coupled to a latch clock to load data during a time that their respective latch clock is in an active state. A selector circuit is coupled to outputs of the first set of storage elements and outputs a value from one output based on a second address input. The second scan latch then loads data from the selector's output during the first part of the input clock period.

A test and measurement system includes an instrument having an input port structured to receive an input signal from a Device Under Test (DUT), a memory structured to store data derived from the input signal, a remote access manager, and an instrument state manager structured to maintain a present operating state of the instrument. The system further includes a remote device structured to receive through a communication network at least a portion of the stored data derived from the input signal from the instrument, and further structured to receive a transaction identifier that identifies the present operating state of the instrument when the portion of the stored data was acquired by the instrument. Methods are also described.

A test and measurement system includes an instrument having an input port structured to receive an input signal from a Device Under Test (DUT), a memory structured to store data derived from the input signal, a remote access manager, and an instrument state manager structured to maintain a present operating state of the instrument. The system further includes a remote device structured to receive through a communication network at least a portion of the stored data derived from the input signal from the instrument, and further structured to receive a transaction identifier that identifies the present operating state of the instrument when the portion of the stored data was acquired by the instrument. Methods are also described.

A machine-learning-based prognostic and health management system comprises a machine sensor, an instruction receiver, a processor, and an annunciator. The machine sensor is configured to dynamically receive data of a machine under test associated with operations of the machine under test. The instruction receiver is configured to dynamically receive a model-assigning command. The processor is configured to dynamically apply a damage alert machine-learning model corresponding to the model-assigning command for processing the data of the machine under test to predict an anomaly probability of an anomaly occurrence of the machine under test. The processor also dynamically generates, according to the anomaly probability, a damage possibility warning on the machine under test, and determine whether to keep the machine under test running or not.

A machine-learning-based prognostic and health management system comprises a machine sensor, an instruction receiver, a processor, and an annunciator. The machine sensor is configured to dynamically receive data of a machine under test associated with operations of the machine under test. The instruction receiver is configured to dynamically receive a model-assigning command. The processor is configured to dynamically apply a damage alert machine-learning model corresponding to the model-assigning command for processing the data of the machine under test to predict an anomaly probability of an anomaly occurrence of the machine under test. The processor also dynamically generates, according to the anomaly probability, a damage possibility warning on the machine under test, and determine whether to keep the machine under test running or not.