Abstract: An example system includes at least one memristive dot product engine (DPE) having at least one resource, the DPE further having a physical interface and a controller, the controller being communicatively coupled to the physical interface, the physical interface to communicate with the controller to access the DPE, and at least one replicated interface, each replicated interface being associated with a virtual DPE, the replicated interface with communicatively coupled to the controller. The controller is to allocate timeslots to the virtual DPE through the associated replicated interface to allow the virtual DPE access to the at least one resource.

Abstract: Techniques for reallocating a memory pending queue based on stalls are provided. In one aspect, it may be determined at a memory stop of a memory fabric that at least one class of memory access is stalled. It may also be determined at the memory stop of the memory fabric that there is at least one class of memory access that is not stalled. At least a portion of a memory pending queue may be reallocated from the class of memory access that is not stalled to the class of memory access that is stalled.

Abstract: A method for allocating resources includes determining that an initial allocation of memory bandwidth for one or more computing jobs fails a performance metric. The memory bandwidth provides access to a global memory pool for multiple legacy processors across a memory fabric. The method also includes determining a new allocation of memory bandwidth for the computing jobs that meets the performance metric. Additionally, the method includes assigning the new allocation of memory bandwidth to the computing jobs. The method further includes executing the computing jobs using the new allocation of memory bandwidth.

Abstract: Disclosed techniques provide for dynamically changing precision of a multi-stage compute process. For example, changing neural network (NN) parameters on a per-layer basis depending on properties of incoming data streams and per-layer performance of an NN among other considerations. NNs include multiple layers that may each be calculated with a different degree of accuracy and therefore, compute resource overhead (e.g., memory, processor resources, etc.). NNs are usually trained with 32-bit or 16-bit floating-point numbers. Once trained, an NN may be deployed in production. One approach to reduce compute overhead is to reduce parameter precision of NNs to 16 or 8 for deployment. The conversion to an acceptable lower precision is usually determined manually before deployment and precision levels are fixed while deployed.

Abstract: An example system includes at least one memristive dot product engine (DPE) having at least one resource, the DPE further having a physical interface and a controller, the controller being communicatively coupled to the physical interface, the physical interface to communicate with the controller to access the DPE, and at least one replicated interface, each replicated interface being associated with a virtual DPE, the replicated interface with communicatively coupled to the controller. The controller is to allocate timeslots to the virtual DPE through the associated replicated interface to allow the virtual DPE access to the at least one resource.

Abstract: Techniques for reallocating a memory pending queue based on stalls are provided. In one aspect, it may be determined at a memory stop of a memory fabric that at least one class of memory access is stalled. It may also be determined at the memory stop of the memory fabric that there is at least one class of memory access that is not stalled. At least a portion of a memory pending queue may be reallocated from the class of memory access that is not stalled to the class of memory access that is stalled.

Abstract: Techniques for reallocating a memory pending queue based on stalls are provided. In one aspect, it may be determined at a memory stop of a memory fabric that at least one class of memory access is stalled. It may also be determined at the memory stop of the memory fabric that there is at least one class of memory access that is not stalled. At least a portion of a memory pending queue may be reallocated from the class of memory access that is not stalled to the class of memory access that is stalled.

Abstract: Techniques for reallocating a memory pending queue based on stalls are provided. In one aspect, it may be determined at a memory stop of a memory fabric that at least one class of memory access is stalled. It may also be determined at the memory stop of the memory fabric that there is at least one class of memory access that is not stalled. At least a portion of a memory pending queue may be reallocated from the class of memory access that is not stalled to the class of memory access that is stalled.

Abstract: According to an example, data for a memcached server is replicated to a memcached replication server. Data operations for the memcached server may be filtered for backing up data to the memcached replication server.

Abstract: Disclosed herein are an apparatus, an integrated circuit, and method to cache objects. At least one hash table of a circuit comprises a predetermined arrangement that maximizes cache memory space and minimizes a number of cache memory transactions. The circuit handles requests by a remote device to obtain or cache an object.

Abstract: According to an example, data for a memcached server is replicated to a memcached replication server. Data operations for the memcached server may be filtered for backing up data to the memcached replication server.