Abstract: An integrated circuit (IC) test engine generates single cycle test patterns for testing for candidate faults and/or defects of a first set of static faults and/or defects of an IC design. A diagnostics engine receives single cycle test result data characterizing application of the single cycle test patterns to a fabricated IC chip based on the IC design and fault-simulates a subset of the single cycle test patterns against a fault model that includes multicycle faults and/or defects utilizing sim-shifting to diagnose a second set of static faults and/or defects in the fabricated IC chip that are only detectable with multicycle test patterns. The diagnostics engine further scores candidate faults and/or defects in the first set of static faults and/or defects and the second set of static faults and/or defects for applicable test patterns to determine a most likely fault and/or defect present in the fabricated IC chip.

Abstract: An instruction storage circuit within a processor that includes an instruction memory and a memory control circuit. The instruction memory is configured to store instructions of a program for the processor. The memory control circuit is configured to receive a particular instruction from the instruction memory, detect a data integrity error in the particular instruction, and generate and store a corrected version of the particular instruction in an error storage circuit within the instruction memory. A flush of an execution pipeline may be performed in response to the error. In response to a refetch of the particular instruction after the pipeline flush, the instruction storage circuit may be configured to cause the particular instruction to be provided from the error storage circuit to the execution pipeline to permit forward progress of the processor.

Abstract: A method and system for performing a duty cycle correction and quadrature error correction for a quarter-rate architecture TX/RX communication system, including correcting a duty cycle error between a first clock signal and a second clock signal, and correcting a quadrature error between a third clock signal and a fourth clock signal.

Abstract: Various embodiments provide for routing a circuit design using routing congestion based on fractional via cost, via density, or both in view of one or more design rules. For instance, some embodiments model via cost based on one or more design rules to determine routing congestion, where routing demand (e.g., routing capacity occupied by) of a via is fractional to the amount of the track blocked by the via. Additionally, some embodiments apply via density modeling based on one or more design rules to determine a routing demand of a via for routing congestion.

Abstract: A method of low-latency and encrypted hardware layer communication includes calculating, by an encryption circuit of a communication bridge controller, a pre-calculated encryption keys corresponding to a block encryptor of the encryption circuit, each block encryptor configured to use a corresponding pre-calculated encryption key to encrypt a corresponding unencrypted data block of a data transmission having one or more unencrypted data blocks, storing the one or more pre-calculated encryption keys in an encryption key memory associated with the communication bridge, for each unecrypted data block, encrypting the unencrypted data block using the corresponding pre-calculated encryption key to generate an encrypted data block and an authentication code block for the unencrypted data block, aggregating one or more encrypted data blocks into an encrypted data transmission, and generating an authenticated code corresponding to the encrypted data transmission based upon each of the authentication code blocks of eac

Abstract: A computer implemented method may include executing a first simulation test for testing a device under test (DUT) and a corresponding test environment; saving a snapshot image of the DUT and of the corresponding test environment upon completion of initialization actions included in the first simulation test to configure the DUT; compiling a DUT part of a second simulation test into the saved snapshot image of the DUT to obtain a restore image for the DUT; loading the restore image of the DUT and restoring the snapshot image of the test environment; loading a test environment part of the second simulation test; and executing the second simulation test on the DUT and corresponding test environment.

Abstract: Embodiments include herein are directed towards a double data rate (“DDR”) controller system. Embodiments may include a plurality of read data buffers, wherein each of the plurality of read data buffers is configured for read data storage and is of a same size. Embodiments may further include a port read response queue that stores information corresponding to an incoming read and a command queue configured to receive read data buffer state information from the port read response queue. Embodiments may also include a read data buffer allocation tracker configured to track a state of each of the plurality of read data buffers.

Abstract: The present disclosure relates to a system and method for use in an electronic circuit design. Embodiments may include receiving, using a processor, one or more DFM rules files from at least one PCB fabricator and importing the one or more DFM rules files to a DFM rule aggregator database. Embodiments may also include grouping one or more rules associated with the one or more DFM rules files using an automated or manual operation. Embodiments may further include performing automatic or manual rule aggregation on the grouped rules based upon, at least in part, rules aggregation information including a DFM template file.

Abstract: An approach includes receiving a machine learning processing job, executing the machine learning processing job using parallel processing of multiple output pixels each cycle by walking data across processing elements with broadcast weights within regions and executing parallel multiplication operations, and generating an output indicating whether the machine learning processing job was successful or failed. In some embodiments, a schedule of actions is generated for respective machine learning processing jobs. The schedule of actions may include any of a plurality of shift operations in a many to many arrangement or a one to many arrangement, shifting data across region boundaries, fetching data and weights from a memory and distribution thereof to a plurality of regions (e.g., weights are distributed to respective weight memories which subsequently broadcasts those weights in a specified order based on a schedule of actions, and where data is distributed to respective processing elements).

Abstract: An approach is disclosed herein for balancing layer densities in using an automated process. The approach disclosed herein operates on a region-by-region and layer-by-layer basis to perform parameterized layer balancing. In some embodiments, the process comprises determining densities of respective layers in respective regions, evaluating each layer and region to determine whether operations need to be taken to balance those layers in the corresponding regions, determining what those actions should be, and then implementing those actions. Additionally, in some embodiments, the process may operate in different orders and may be associated with a looping flow until a layout being processed has been balanced.

Abstract: An approach includes a method, product, and apparatus for dynamically removing sparse data on a pixel by pixel basis. In some embodiments, a machine learning processing job is received. The machine learning processing job is then executed on a pixel by pixel basis by selecting non-zero data values for input into a systolic array, wherein sparse data is not selected for input into the systolic array. Subsequently, a message is generated that provides an indication of whether the execution completed successfully. In some embodiments, the machine learning processing job comprises at least a plurality of multiply and accumulate operations. In some embodiments, at least one data value equal to zero for the machine learning processing job is not input into a systolic array. In some embodiments, a plurality of weights are input into a plurality of columns for each cycle.

Abstract: A system includes a machine configured to perform operations including accessing an integrated circuit design including a buffer tree that interconnects a plurality of inputs and buffers. The buffer tree includes a baseline timing characteristic. The operations include identifying a set of candidate solutions for improving the baseline timing characteristic using an initial timing model and selecting a subset of candidate solutions that have a timing characteristic lower than the baseline timing characteristic. Then the subset of candidate solutions are evaluated using a detailed timing model and based on determining that at least one candidate solution in the subset has a timing characteristic that is better than the baseline timing characteristic, selecting a candidate solution from the set of candidate solutions, and updating the buffer tree based on the candidate solution.

Abstract: Various embodiments provide for routing a net of a circuit design using rule-based routing blockage extension, which may be part of electronic design automation (EDA). In particular, some embodiments route a net of a circuit design by determining a dimension extension value based on a design rule of the circuit design and applying the dimension extension value to at least one existing routing blockage.

Abstract: Various embodiments provide for determining a capacitance (or capacitor value) of a circuit, determining a resistance-capacitance time constant (or RC time constant) of a circuit, or both. The circuit can comprise an integrated circuit (IC), such as a circuit implemented on die. An IC of some embodiments generates a frequency of a dock wave signal (e.g., an output signal) such that the clock wave signal encodes an effective capacitance of the IC, a RC time constant of the IC, or both. A component external to the IC, such as a controller, can receive the clock wave signal and determine the effective capacitance of the IC, the RC time constant of the IC, or both based on the received clock wave signal.

Abstract: Various embodiments provide for a data sampler with built-in decision feedback equalization (DFE) and offset cancellation. For some embodiments, two or more data samplers described herein can be used to implement a data signal receiver circuit, which can use those two or more data samplers to facilitate half-rate or quarter-rate data sampling.

Abstract: Using a buffer sized according to the size of the filters of a convolutional neural network (CNN), a processor may use a read pointer to generate a two-dimensional virtual matrix of inputs. The number of inputs in each row in the two-dimensional virtual matrix of inputs may match the one-dimensional filter size of the cubic filters. The processor may collapse each of the cubic filters to one-dimensional linear arrays and generate a two-dimensional filter matrix from the one-dimensional linear arrays. The convolution computations for a corresponding layer of the CNN therefore reduce to a single matrix multiplication without any memory movement operations. When the buffer is refreshed using a new input frame, the processor may increment the initial read address of each read pointer by one and increment the final read address by one, circling back to the corresponding initial read address.

Abstract: Embodiments include herein are directed towards a dynamic random access memory system. Embodiments may include a command queue that is configured to hold all commands that are currently selectable for bank operation and execution. Embodiments may further include bank logic operatively connected with the command queue. The bank logic may include a bank management module and a plurality of bank slices, wherein each of the plurality of bank slices is an independent, re-assignable bank tracking module.

Abstract: Techniques are disclosed relating to an apparatus, including a data storage circuit having a plurality of entries, and a load-store pipeline configured to allocate an entry in the data storage circuit in response to a determination that a first instruction includes an access to an external memory circuit. The apparatus further includes an execution pipeline configured to make a determination, while performing a second instruction and using the entry in the data storage circuit, that the second instruction uses a result of the first instruction, and cease performance of the second instruction in response to the determination.

Abstract: An approach is described for a method, product, and apparatus for a machine learning process using dynamic rearrangement of sparse data and corresponding weights. This approach includes a method, product, and apparatus for dynamically rearranging input data to move sparse data to a location such that computations on the sparse data might be avoided when executing a machine learning processing job. For example, sparse data within each row of the input matrix can be moved to the end of each corresponding row. When the input data is folded to fit the array, that sparse data might be at least partially contained within a fold that comprises only sparse data and possibly filler data. In such an event, computations on the fold are unnecessary and are avoided. In some embodiments, the approach includes dynamically rearranging a weight matrix to maintain a correspondence between the input data and the weights.

Abstract: Aspects of the present disclosure address systems and methods for performing a machine-learning based clustering of clock sinks during clock tree synthesis. An integrated circuit (IC) design comprising a clock net that includes a plurality of clock sinks is accessed. An initial number of clusters to generate from the set of clock sinks is determined using a machine-learning model. A first set of clusters is generated from the set of clocks sinks and includes the initial number of clusters. A timing analysis is performed to determine whether each cluster in the first set of clusters satisfies design rule constraints. The initial number of clusters is adjusted based on the timing analysis and a clustering solution is generated based on the adjusted number of clusters.