A system comprising a row hammer mitigation circuitry and a cache memory that collaborate to mitigate row hammer attacks on a memory media device is described. The cache memory biases cache policy based on row access count information maintained by the row hammer mitigation circuit. The row hammer mitigation circuitry may be implemented in a memory controller. The memory media device may be DRAM. Corresponding methods are also described.

Systems and methods for cache optimization are disclosed. A request for a user interface is received from a first user device. The request includes a user key. An interface key corresponding to an interface template of the requested user interface is generated from the user key. The interface template of the requested user interface is loaded. The interface template includes one or more edge side include (ESI) identifiers in the interface template. An element key corresponding to a first ESI element associated with a first of the one or more ESI identifiers is generated from the user key. The first ESI element is loaded and positioned at a location within the interface template identified by the first of the one or more ESI identifiers. A complete user interface is provided to the first user device. The complete user interface includes the interface template having the first ESI element positioned therein.

Systems and methods for cache optimization are disclosed. A request for a user interface is received from a first user device. The request includes a user key. An interface key corresponding to an interface template of the requested user interface is generated from the user key. The interface template of the requested user interface is loaded. The interface template includes one or more edge side include (ESI) identifiers in the interface template. An element key corresponding to a first ESI element associated with a first of the one or more ESI identifiers is generated from the user key. The first ESI element is loaded and positioned at a location within the interface template identified by the first of the one or more ESI identifiers. A complete user interface is provided to the first user device. The complete user interface includes the interface template having the first ESI element positioned therein.

The present disclosure provides a write cache circuit, a data write method, and a memory. The write cache circuit includes: a control circuit configured to generate, on the basis of a mask write instruction, a first write pointer and a pointer to be positioned, generate a second write pointer on the basis of a write command, generate a first output pointer on the basis of a mask write shift instruction, and generate a second output pointer on the basis of a write shift instruction; a first cache circuit configured to cache, on the basis of the first write pointer, the pointer to be positioned and output a positioned pointer on the basis of the first output pointer, the positioned pointer being configured to instruct a second cache circuit to output a write address written by the second write pointer generated according to the mask write instruction.

The present disclosure provides a write cache circuit, a data write method, and a memory. The write cache circuit includes: a control circuit configured to generate, on the basis of a mask write instruction, a first write pointer and a pointer to be positioned, generate a second write pointer on the basis of a write command, generate a first output pointer on the basis of a mask write shift instruction, and generate a second output pointer on the basis of a write shift instruction; a first cache circuit configured to cache, on the basis of the first write pointer, the pointer to be positioned and output a positioned pointer on the basis of the first output pointer, the positioned pointer being configured to instruct a second cache circuit to output a write address written by the second write pointer generated according to the mask write instruction.

Methods for implementing or synthesizing functions in hardware and fixed-function hardware include generating a look-up table, LUT, representing the function and then applying a transform to the LUT to transform the LUT into a plurality of derived LUTs. The transform may be applied recursively. A hardware design implementing each of the derived LUTs in fixed-function hardware logic, along with a logic unit that performs the inverse transform, is then created.

Methods for implementing or synthesizing functions in hardware and fixed-function hardware include generating a look-up table, LUT, representing the function and then applying a transform to the LUT to transform the LUT into a plurality of derived LUTs. The transform may be applied recursively. A hardware design implementing each of the derived LUTs in fixed-function hardware logic, along with a logic unit that performs the inverse transform, is then created.

A streaming engine employed in a digital data processor specifies a fixed read only data stream defined by plural nested loops. An address generator produces address of data elements. A steam head register stores data elements next to be supplied to functional units for use as operands. The streaming engine stores an early address of next to be fetched data elements and a late address of a data element in the stream head register for each of the nested loops. The streaming engine stores an early loop counts of next to be fetched data elements and a late loop counts of a data element in the stream head register for each of the nested loops.

Disclosed are systems and methods that determine whether instances of data (e.g., forward activations, backward derivatives of activations) that are used to train deep neural networks are to be stored on-chip or off-chip. The disclosed systems and methods are also used to prune the data (discard or delete selected instances of data). A system includes a hierarchical arrangement of on-chip and off-chip memories, and also includes a hierarchical arrangement of data selector devices that are used to decide whether to discard data and where in the system the data is to be discarded.

Disclosed are systems and methods that determine whether instances of data (e.g., forward activations, backward derivatives of activations) that are used to train deep neural networks are to be stored on-chip or off-chip. The disclosed systems and methods are also used to prune the data (discard or delete selected instances of data). A system includes a hierarchical arrangement of on-chip and off-chip memories, and also includes a hierarchical arrangement of data selector devices that are used to decide whether to discard data and where in the system the data is to be discarded.