Abstract: An apparatus and method are described for distributed and cooperative computation in artificial neural networks. For example, one embodiment of an apparatus comprises: an input/output (I/O) interface; a plurality of processing units communicatively coupled to the I/O interface to receive data for input neurons and synaptic weights associated with each of the input neurons, each of the plurality of processing units to process at least a portion of the data for the input neurons and synaptic weights to generate partial results; and an interconnect communicatively coupling the plurality of processing units, each of the processing units to share the partial results with one or more other processing units over the interconnect, the other processing units using the partial results to generate additional partial results or final results. The processing units may share data including input neurons and weights over the shared input bus.

Abstract: An apparatus and method are described for distributed and cooperative computation in artificial neural networks. For example, one embodiment of an apparatus comprises: an input/output (I/O) interface; a plurality of processing units communicatively coupled to the I/O interface to receive data for input neurons and synaptic weights associated with each of the input neurons, each of the plurality of processing units to process at least a portion of the data for the input neurons and synaptic weights to generate partial results; and an interconnect communicatively coupling the plurality of processing units, each of the processing units to share the partial results with one or more other processing units over the interconnect, the other processing units using the partial results to generate additional partial results or final results. The processing units may share data including input neurons and weights over the shared input bus.

Abstract: An apparatus and method are described for distributed and cooperative computation in artificial neural networks. For example, one embodiment of an apparatus comprises: an input/output (I/O) interface; a plurality of processing units communicatively coupled to the I/O interface to receive data for input neurons and synaptic weights associated with each of the input neurons, each of the plurality of processing units to process at least a portion of the data for the input neurons and synaptic weights to generate partial results; and an interconnect communicatively coupling the plurality of processing units, each of the processing units to share the partial results with one or more other processing units over the interconnect, the other processing units using the partial results to generate additional partial results or final results. The processing units may share data including input neurons and weights over the shared input bus.

Abstract: A combination of hardware and software collect profile data for asynchronous events, at code region granularity. An exemplary embodiment is directed to collecting metrics for prefetching events, which are asynchronous in nature. Instructions that belong to a code region are identified using one of several alternative techniques, causing a profile bit to be set for the instruction, as a marker. Each line of a data block that is prefetched is similarly marked. Events corresponding to the profile data being collected and resulting from instructions within the code region are then identified. Each time that one of the different types of events is identified, a corresponding counter is incremented. Following execution of the instructions within the code region, the profile data accumulated in the counters are collected, and the counters are reset for use with a new code region.

Abstract: A computer-readable storage medium, method and system for optimization-level aware branch prediction is described. A gear level is assigned to a set of application instructions that have been optimized. The gear level is also stored in a register of a branch prediction unit of a processor. Branch prediction is then performed by the processor based upon the gear level.

Abstract: A processor includes a front end, a decoder, an allocator, and a retirement unit. The decoder includes logic to identify an end-of-live-range (EOLR) indicator. The EOLR indicator specifies an architectural register and a location in code for which the architectural register is unused. The allocator includes logic to scan for a mapping of the architectural register to a physical register, based upon the EOLR indicator. The allocator also includes logic to generate a request to disassociate the architectural register from the physical register. The retirement unit includes logic to disassociate the architectural register from the physical register.

Abstract: A combination of hardware and software collect profile data for asynchronous events, at code region granularity. An exemplary embodiment is directed to collecting metrics for prefetching events, which are asynchronous in nature. Instructions that belong to a code region are identified using one of several alternative techniques, causing a profile bit to be set for the instruction, as a marker. Each line of a data block that is prefetched is similarly marked. Events corresponding to the profile data being collected and resulting from instructions within the code region are then identified. Each time that one of the different types of events is identified, a corresponding counter is incremented. Following execution of the instructions within the code region, the profile data accumulated in the counters are collected, and the counters are reset for use with a new code region.

Abstract: A storage device and method are described for performing convolution operations. For example, one embodiment of an apparatus to perform convolution operations comprises a plurality of processing units to execute convolution operations on input data and partial results; a unified scratchpad memory comprising a plurality of memory banks communicatively coupled to the plurality of processing units through a plurality of read/write ports, each of the plurality of memory banks partitioned to store both the input data and partial results; a control unit to allocate the input data and partial results to the memory banks to ensure a minimum quality of service in accordance with the specified number of read/write ports and the specified convolution operation to be performed.

Abstract: Disclosed is an apparatus and method to manage instruction cache prefetching from an instruction cache. A processor may comprise: a prefetch engine; a branch prediction engine to predict the outcome of a branch; and dynamic optimizer. The dynamic optimizer may be used to control: identifying common instruction cache misses and inserting a prefetch instruction from the prefetch engine to the instruction cache.

Abstract: An apparatus and method are described for distributed and cooperative computation in artificial neural networks. For example, one embodiment of an apparatus comprises: an input/output (I/O) interface; a plurality of processing units communicatively coupled to the I/O interface to receive data for input neurons and synaptic weights associated with each of the input neurons, each of the plurality of processing units to process at least a portion of the data for the input neurons and synaptic weights to generate partial results; and an interconnect communicatively coupling the plurality of processing units, each of the processing units to share the partial results with one or more other processing units over the interconnect, the other processing units using the partial results to generate additional partial results or final results. The processing units may share data including input neurons and weights over the shared input bus.

Abstract: A storage device and method are described for performing convolution operations. For example, one embodiment of an apparatus to perform convolution operations comprises a plurality of processing units to execute convolution operations on input data and partial results; a unified scratchpad memory comprising a plurality of memory banks communicatively coupled to the plurality of processing units through a plurality of read/write ports, each of the plurality of memory banks partitioned to store both the input data and partial results; a control unit to allocate the input data and partial results to the memory banks to ensure a minimum quality of service in accordance with the specified number of read/write ports and the specified convolution operation to be performed.

Abstract: A processor includes a front end, a decoder, an allocator, and a retirement unit. The decoder includes logic to identify an end-of-live-range (EOLR) indicator. The EOLR indicator specifies an architectural register and a location in code for which the architectural register is unused. The allocator includes logic to scan for a mapping of the architectural register to a physical register, based upon the EOLR indicator. The allocator also includes logic to generate a request to disassociate the architectural register from the physical register. The retirement unit includes logic to disassociate the architectural register from the physical register.

Abstract: A processor includes a processor core and a calculation circuit. The processor core includes logic determine a set of weights for use in a convolutional neural network (CNN) calculation and scale up the weights using a scale value. The calculation circuit includes logic to receive the scale value, the set of weights, and a set of input values, wherein each input value and associated weight of a same fixed size. The calculation circuit also includes logic to determine results from convolutional neural network (CNN) calculations based upon the set of weights applied to the set of input values, scale down the results using the scale value, truncate the scaled down results to the fixed size, and communicatively couple the truncated results to an output for a layer of the CNN.

Abstract: Techniques and mechanisms for a processor to determine whether a commit action is to be performed. In an embodiment, a processor performs operations to determine whether a commit instruction is for contingent performance of a commit action. In another embodiment, one or more conditions of processor state are evaluated in response to determining that the commit instruction is for contingent performance of the commit action, where the evaluation is performed to determine whether the commit action indicated by the commit instruction is to be performed.

Abstract: A computer-readable storage medium, method and system for optimization-level aware branch prediction is described. A gear level is assigned to a set of application instructions that have been optimized. The gear level is also stored in a register of a branch prediction unit of a processor. Branch prediction is then performed by the processor based upon the gear level.

Abstract: Disclosed is an apparatus and method to manage instruction cache prefetching from an instruction cache. A processor may comprise: a prefetch engine; a branch prediction engine to predict the outcome of a branch; and dynamic optimizer. The dynamic optimizer may be used to control: indentifying common instruction cache misses and inserting a prefetch instruction from the prefetch engine to the instruction cache.

Abstract: A combination of hardware and software collect profile data for asynchronous events, at code region granularity. An exemplary embodiment is directed to collecting metrics for prefetching events, which are asynchronous in nature. Instructions that belong to a code region are identified using one of several alternative techniques, causing a profile bit to be set for the instruction, as a marker. Each line of a data block that is prefetched is similarly marked. Events corresponding to the profile data being collected and resulting from instructions within the code region are then identified. Each time that one of the different types of events is identified, a corresponding counter is incremented. Following execution of the instructions within the code region, the profile data accumulated in the counters are collected, and the counters are reset for use with a new code region.

Abstract: Disclosed is an apparatus and method generally related to controlling a multimedia extension control and status register (MXCSR). A processor core may include a floating point unit (FPU) to perform arithmetic functions; and a multimedia extension control register (MXCR) to provide control bits to the FPU. Further an optimizer may be used to select a speculative multimedia extension status register (SPEC_MXSR) from a plurality of SPEC_MXSRs to update a multimedia extension status register (MXSR) based upon an instruction.

Abstract: Techniques are described for providing an enhanced cache coherency protocol for a multi-core processor that includes a Speculative Request For Ownership Without Data (SRFOWD) for a portion of cache memory. With a SRFOWD, only an acknowledgement message may be provided as an answer to a requesting core. The contents of the affected cache line are not required to be a part of the answer. The enhanced cache coherency protocol may assure that a valid copy of the current cache line exists in case of misspeculation by the requesting core. Thus, an owner of the current copy of the cache line may maintain a copy of the old contents of the cache line. The old contents of the cache line may be discarded if speculation by the requesting core turns out to be correct. Otherwise, in case of misspeculation by the requesting core, the old contents of the cache line may be set back to a valid state.