A computer system configured to enable communication between two or more virtual platforms is disclosed. The computer system comprises a physical processor configured to run the two or more virtual platforms. The computer system further comprises a memory. The memory comprises one or more separate memory portions allocated to each of the two or more virtual platforms, wherein within at least one memory portion allocated to one of the virtual platform a predefined range of addresses is configured as a shared device memory, the shared device memory being accessible by all the virtual platforms. Firmware running on a first virtual platform is configured to transfer a data packet from the first virtual platform to one or more further virtual platforms via the shared device memory.

A process is disclosed to identify the minimal set of sequential and combinational signals needed to fully reconstruct the combinational layout after emulation is complete. A minimal subset of sequential and combinational elements is output from the emulator to maximize the emulator speed and limit the utilization of emulator resources, e.g., FPGA resources. An efficient reconstruction of combinational waveforms or SAIF data is performed using a parallel computing grid.

A method and system for generating and managing virtual industrial devices in an industrial network is disclosed. The method includes capturing data packets associated with an ongoing industrial communication between an industrial application and an industrial device. The method further includes segregating the captured data packets into one or more requests and the one or more responses by analyzing information included in the data packets. The method also includes storing the one or more requests along with the one or more responses for the ongoing industrial communication in memory. The method includes dynamically generating a virtual industrial device emulating the industrial device based on the stored one or more requests and the stored one or more responses. The method includes establishing a communication session between the generated virtual industrial device and the industrial application for performing one or more test operations on the generated virtual industrial device.

Methods, apparatus, systems, and articles of manufacture are disclosed to debug a hardware accelerator such as a neural network accelerator for executing Artificial Intelligence computational workloads. An example apparatus includes a core with a core input and a core output to execute executable code based on a machine-learning model to generate a data output based on a data input, and debug circuitry coupled to the core. The debug circuitry is configured to detect a breakpoint associated with the machine-learning model, compile executable code based on at least one of the machine-learning model or the breakpoint. In response to the triggering of the breakpoint, the debug circuitry is to stop the execution of the executable code and output data such as the data input, data output and the breakpoint for debugging the hardware accelerator.

Embodiments of the present disclosure provide a method, a system, and a computer readable storage medium for detecting deviations from targeted design performance in accelerator/emulator environment. In an embodiment, the method comprises loading target vales for a performance metric onto a hardware-accelerated simulator; setting breakpoints to pause the simulator at defined intervals; simulating, by the hardware-accelerated simulator, execution of a circuit design. The method further comprises during the simulating, using said breakpoints to pause the simulating at the defined intervals, and during each pause, comparing a measured value for the performance metric to the target value for the performance metric; and ending the simulation when a specified condition based on said comparing is met. In embodiments, when a difference between the measured value for the performance metric and the target value for the performance metric is within a preset tolerance, the pause is ended and the simulation continues.

A trace subsystem of an emulation system may generate differential frame data based upon successive frames. If one compression mode, the trace subsystem may set a flag bit and store differential frame data if there is at least one non-zero bit in the differential frame data. If the differential frame data includes only zero bits, the trace subsystem may set the flag bit without storing the frame data. In another compression mode, the computer may further compress the differential data if the frame data includes one (one-hot) or two (two-hot) non-zero bits. The controller may set flag bits to indicate one of all-zeroes, one-hot, two-hot, and random data conditions (more than two non-zero bits). For one-hot or two-hot conditions, the controller may store bits indicating the positions of the non-zero bits. For random data conditions, the controller may store the entire differential frame.

Mapping input locations to enable execution of second executable code using trace data gathered during execution of first executable code. A trace of a prior execution of the first code, and the second code, are accessed. The trace stores data of an input that was consumed by first executable instructions of the first code. It is determined that the stored data of the input is usable as an input to second executable instructions of the second code. A difference in how the first instructions accessed the input during recording, as compared to how the second instructions expect to access input, is identified. Based on the identified difference, a location transformation is determined that would enable the second instructions to access the stored data. Execution of the second instructions is emulated using the stored data, including projecting the location transformation to enable the second instructions to access the stored data.

An in-circuit emulator device includes a CPU that executes a program, and outputs or inputs/outputs parameter values that change due to a program being executed, a plurality of trace memories that sequentially store the parameter values outputted by or inputted/outputted to/by the CPU to form a change history of the parameter values, an event detection circuit that detects a specific event that occurs as the CPU executes the program, and an event trace control circuit that stops a storage operation of any one of the plurality of trace memories in response to detection of the specific event by the event detection circuit, and reads and outputs the change history of the parameter value from the one trace memory.

A process is disclosed to identify the minimal set of sequential and combinational signals needed to fully reconstruct the combinational layout after emulation is complete. A minimal subset of sequential and combinational elements is output from the emulator to maximize the emulator speed and limit the utilization of emulator resources, e.g., FPGA resources. An efficient reconstruction of combinational waveforms or SAIF data is performed using a parallel computing grid.

Methods, apparatus, systems, and articles of manufacture are disclosed to debug a hardware accelerator such as a neural network accelerator for executing Artificial Intelligence computational workloads. An example apparatus includes a core with a core input and a core output to execute executable code based on a machine-learning model to generate a data output based on a data input, and debug circuitry coupled to the core. The debug circuitry is configured to detect a breakpoint associated with the machine-learning model, compile executable code based on at least one of the machine-learning model or the breakpoint. In response to the triggering of the breakpoint, the debug circuitry is to stop the execution of the executable code and output data such as the data input, data output and the breakpoint for debugging the hardware accelerator.