Abstract: In accordance with the described techniques for bank-level parallelism for processing in memory, a plurality of commands are received for execution by a processing in memory component embedded in a memory. The memory includes a first bank and a second bank. The plurality of commands include a first stream of commands which cause the processing in memory component to perform operations that access the first bank and a second stream of commands which cause the processing in memory component to perform operations that access the second bank. A next row of the first bank that is to be accessed by the processing in memory component is identified. Further, a precharge command is scheduled to close a first row of the first bank and an activate command is scheduled to open the next row of the first bank in parallel with execution of the second stream of commands.

Abstract: In accordance with described techniques for reduction of parallel memory operation messages, a computing system or computing device includes a memory system that receives memory operation messages. A shared response component in the memory system receives responses to the memory operation messages, and identifies a set of the responses that are coalesceable. The shared response component then coalesces the set of the responses into a combined message for communication completion through a communication path in the memory system.

Abstract: In accordance with described techniques for filtered responses to memory operation messages, a computing system or computing device includes a memory system that receives messages. A filter component in the memory system receives the responses to the memory operation messages, and filters one or more of the responses based on a filterable condition. A tracking logic component tracks the one or more responses as filtered responses for communication completion.

Abstract: A virtual padding unit provides a virtual padded data structure (e.g., virtually padded matrix) that provides output values for a padded data structure without storing all of the padding elements in memory. When the virtual padding unit receives a virtual memory address of a location in the virtual padded data structure, the virtual padding unit checks whether the location is a non-padded location in the virtual padded data structure or a padded location in the virtual padded data structure. If the location is a padded location in the virtual padded data structure, the virtual padding unit outputs a padding value rather than a value stored in the virtual padded data structure. If the location is a non-padded location in the virtual padded data structure, a value stored at the location is output.

Abstract: An encryption circuit includes an iterative block cipher circuit. The iterative block cipher circuit has a counter input for a row index, a key input for receiving a secret key, and an output for providing an encrypted counter value in response to performing a block cipher process using the row index as a counter the secret key. The encryption circuit uses the iterative block cipher circuit during a row operation to a memory.

Abstract: A system and method for efficiently accessing sparse data for a workload are described. In various implementations, a computing system includes an integrated circuit and a memory for storing tasks of a workload that includes sparse accesses of data items stored in one or more tables. The integrated circuit receives a user query, and generates a result based on multiple data items targeted by the user query. To reduce the latency of processing the workload even with sparse lookup operations performed on the one or more tables, a prefetch engine of the integrated circuit stores a subset of data items in prefetch data storage. The prefetch engine also determines which data items to store in the prefetch data storage based on one or more of a frequency of reuse, a distance or latency of access of a corresponding table of the one more tables, or other.

Abstract: Systems, apparatuses, and methods for dynamically coalescing multi-bank memory commands to improve command throughput are disclosed. A system includes a processor coupled to a memory via a memory controller. The memory also includes processing-in-memory (PIM) elements which are able to perform computations within the memory. The processor generates memory requests targeting the memory which are sent to the memory controller. The memory controller stores commands received from the processor in a queue, and the memory controller determines whether opportunities exist for coalescing multiple commands together into a single multi-bank command. After coalescing multiple commands into a single combined multi-bank command, the memory controller conveys, across the memory bus to multiple separate banks, the single multi-bank command and a multi-bank code specifying which banks are targeted. The memory banks process the command in parallel, and the PIM elements process the data next to each respective bank.

Abstract: Systems, apparatuses, and methods for dynamically coalescing multi-bank memory commands to improve command throughput are disclosed. A system includes a processor coupled to a memory via a memory controller. The memory also includes processing-in-memory (PIM) elements which are able to perform computations within the memory. The processor generates memory requests targeting the memory which are sent to the memory controller. The memory controller stores commands received from the processor in a queue, and the memory controller determines whether opportunities exist for coalescing multiple commands together into a single multi-bank command. After coalescing multiple commands into a single combined multi-bank command, the memory controller conveys, across the memory bus to multiple separate banks, the single multi-bank command and a multi-bank code specifying which banks are targeted. The memory banks process the command in parallel, and the PIM elements process the data next to each respective bank.

Abstract: Systems, apparatuses, and methods for dynamically coalescing multi-bank memory commands to improve command throughput are disclosed. A system includes a processor coupled to a memory via a memory controller. The memory also includes processing-in-memory (PIM) elements which are able to perform computations within the memory. The processor generates memory requests targeting the memory which are sent to the memory controller. The memory controller stores commands received from the processor in a queue, and the memory controller determines whether opportunities exist for coalescing multiple commands together into a single multi-bank command. After coalescing multiple commands into a single combined multi-bank command, the memory controller conveys, across the memory bus to multiple separate banks, the single multi-bank command and a multi-bank code specifying which banks are targeted. The memory banks process the command in parallel, and the PIM elements process the data next to each respective bank.

Abstract: Embodiments include computing devices, systems, and methods for task-based handling of repetitive processes in parallel. At least one processor of the computing device, or a specialized hardware controller, may be configured to partition iterations of a repetitive process and assign the partitions to initialized tasks to be executed in parallel by a plurality of processor cores. Upon completing a task, remaining divisible partitions of the repetitive process of ongoing tasks may be subpartitioned and assigned to the ongoing task, and the completed task or a newly initialized task. Information about the iteration space for a repetitive process may be stored in a descriptor table, and status information for all partitions of a repetitive process stored in a status table. Each processor core may have an associated local table that tracks iteration execution of each task, and is synchronized with the status table.

Abstract: Embodiments include computing devices, systems, and methods for task-based handling of repetitive processes in parallel. At least one processor of the computing device, or a specialized hardware controller, may be configured to partition iterations of a repetitive process and assign the partitions to initialized tasks to be executed in parallel by a plurality of processor cores. Upon completing a task, remaining divisible partitions of the repetitive process of ongoing tasks may be subpartitioned and assigned to the ongoing task, and the completed task or a newly initialized task. Information about the iteration space for a repetitive process may be stored in a descriptor table, and status information for all partitions of a repetitive process stored in a status table. Each processor core may have an associated local table that tracks iteration execution of each task, and is synchronized with the status table.