Abstract:
A system is described that performs training operations for a neural network, the system including an analog circuit element functional block with an array of analog circuit elements, and a controller. The controller monitors error values computed using an output from each of one or more initial iterations of a neural network training operation, the one or more initial iterations being performed using neural network data acquired from the memory. When one or more error values are less than a threshold, the controller uses the neural network data from the memory to configure the analog circuit element functional block to perform remaining iterations of the neural network training operation. The controller then causes the analog circuit element functional block to perform the remaining iterations.
Abstract:
A processing system includes a processing device coupled to a memory configured to check for and correct faults in requested data. In response to correcting the faults of the requested data, the memory sends the corrected data and unused check bits to the processing device as a plurality of fetch returns. The memory also sends a parity fetch based on the corrected data and one or more operations to the processing device. After receiving the plurality of fetch returns and the unused check bits, the processing device checks each fetch return for faults based on the unused check bits. In response to determining that a fetch return includes a fault, the processing device erases the fetch return and reconstructs the fetch return based on one or more other received fetch returns and the parity fetch.
Abstract:
Systems, apparatuses, and methods for managing buffers in a neural network implementation with heterogeneous memory are disclosed. A system includes a neural network coupled to a first memory and a second memory. The first memory is a relatively low-capacity, high-bandwidth memory while the second memory is a relatively high-capacity, low-bandwidth memory. During a forward propagation pass of the neural network, a run-time manager monitors the usage of the buffers for the various layers of the neural network. During a backward propagation pass of the neural network, the run-time manager determines how to move the buffers between the first and second memories based on the monitored buffer usage during the forward propagation pass. As a result, the run-time manager is able to reduce memory access latency for the layers of the neural network during the backward propagation pass.
Abstract:
A method and apparatus for predicting and managing a device failure includes responsive to a predicted failure of a memory device, the predicted failure based on sensor data associated with the memory device, determining a further action for the memory device.
Abstract:
A system and method for protecting memory instructions against faults are described. The system and method include converting the slave instructions to dummy operations, modifying memory arbiter to issue up to N master and N slave global/shared memory instructions per cycle, sending master memory requests to memory system, using slave requests for error checking, entering master requests to the GM/LM FIFO, storing slave requests in a register, and comparing the entered master requests with the stored slave requests.
Abstract:
A system comprising an electronic device that includes a processor is described. During operation, the processor acquires a full version of a neural network, the neural network including internal elements for processing instances of input image data having a set of color channels. The processor then generates, from the neural network, a set of sub-networks, each sub-network being a separate copy of the neural network with the internal elements for processing at least one of the color channels in instances of input image data removed, so that each sub-network is configured for processing a different set of one or more color channels in instances of input image data. The processor next provides the sub-networks for processing instances of input image data—and may itself use the sub-networks for processing instances of input image data.
Abstract:
A system and method for protecting memory instructions against faults are described. The system and method include converting the slave instructions to dummy operations, modifying memory arbiter to issue up to N master and N slave global/shared memory instructions per cycle, sending master memory requests to memory system, using slave requests for error checking, entering master requests to the GM/LM FIFO, storing slave requests in a register, and comparing the entered master requests with the stored slave requests.
Abstract:
A system and method for protecting memory instructions against faults are described. The system and method include converting the slave instructions to dummy operations, modifying memory arbiter to issue up to N master and N slave global/shared memory instructions per cycle, sending master memory requests to memory system, using slave requests for error checking, entering master requests to the GM/LM FIFO, storing slave requests in a register, and comparing the entered master requests with the stored slave requests.
Abstract:
A system and method for protecting memory instructions against faults are described. The system and method include converting the slave instructions to dummy operations, modifying memory arbiter to issue up to N master and N slave global/shared memory instructions per cycle, sending master memory requests to memory system, using slave requests for error checking, entering master requests to the GM/LM FIFO, storing slave requests in a register, and comparing the entered master requests with the stored slave requests.
Abstract:
In the described embodiments, a processor core (e.g., a GPU core) receives a section of program code to be executed in a transaction from another entity in a computing device. The processor core sends the section of program code to one or more compute units in the processor core to be executed in a first transaction and concurrently executed in a second transaction, thereby creating a “redundant transaction pair.” When the first transaction and the second transaction are completed, the processor core compares a read-set of the first transaction to a read-set of the second transaction and compares a write-set of the first transaction to a write-set of the second transaction. When the read-sets and the write-sets match and no transactional error condition has occurred, the processor core allows results from the first transaction to be committed to an architectural state of the computing device.