An embodiment method executes instructions, each corresponding to switching a signal, a delay, and a condition selected among first, second, or third conditions. Each execution includes performing, after the delay, switching the signal if the condition is the first condition, if the condition is the second condition and a flag is in an active state, or if the condition is the third condition and the flag is in an inactive state, or not switching the signal if the condition is the second condition and the flag is in the inactive state, or if the condition is the third condition and the flag is in the active state. A first instruction represents a first switching of a first signal, a first delay, and the second condition, and is immediately followed by a second instruction representing the first switching of the first signal, a second delay, and the third condition.

Techniques for task processing using a highly parallel processing architecture with a shallow pipeline are disclosed. A two-dimensional array of compute elements is accessed. Each compute element within the array of compute elements is known to a compiler and is coupled to its neighboring compute elements within the array of compute elements. Control for the array of compute elements is provided on a cycle-by-cycle basis. The control is enabled by a stream of wide, variable length, microcode control words generated by the compiler. Relevant portions of the control word are stored within a cache associated with the array of compute elements. The control words are decompressed. The decompressing occurs cycle-by-cycle out of the cache over multiple cycles. A compiled task is executed on the array of compute elements, based on the decompressing. Simultaneous execution of two or more potential compiled task outcomes is provided.

Apparatuses and methods are provided for in-memory operations. An example apparatus includes a PIM capable device having an array of memory cells and sensing circuitry coupled to the array, where the sensing circuitry includes a sense amplifier and a compute component. The PIM capable device includes timing circuitry selectably coupled to the sensing circuitry. The timing circuitry is configured to control timing of performance of operations performed using the sensing circuitry. The PIM capable device also includes a sequencer selectably coupled to the timing circuitry. The sequencer is configured to coordinate compute operations. The apparatus also includes a source external to the PIM capable device. The sequencer is configured to receive a command instruction set from the source to initiate performance of a compute operation.

A computer uses an active cable architecture to control communications. The computer sends a first set of instructions for completion of an activity to a first micro-controller of an active communication cable. The computer determines that at least one transceiver of an active cable is to receive a set of signals from the first micro-controller. The computer forms a communication connection between the first micro-controller and the at least one transceiver. The computer sends a second set of instructions to the at least one transceiver, wherein the second set of instructions instruct the at least one transceiver to complete at least a portion of the activity.

In the disclosure, the microprocessor resolves the conflicts in decode stage and schedules the instruction to be executed at a future time. The instruction is issued to an execution queue until the scheduled time in the future when it is dispatched to a functional unit for execution. The disclosure uses a counter for the functional unit to track when the resource is available in the future to accept the next instruction. The disclosure also tracks the future N cycles when the register file read and write ports are scheduled to read and write operand data.

Recovering microprocessor logical register values by: partitioning a register mapper by logical register type; providing a plurality of recovery ports; assigning a logical register type to a recovery port; receiving a restore required instruction; and mapping SRB (save and restore buffer) values to the register mapper by logical register type.

Apparatuses and methods are provided for in-memory operations. An example apparatus includes a PIM capable device having an array of memory cells and sensing circuitry coupled to the array, where the sensing circuitry includes a sense amplifier and a compute component. The PIM capable device includes timing circuitry selectably coupled to the sensing circuitry. The timing circuitry is configured to control timing of performance of operations performed using the sensing circuitry. The PIM capable device also includes a sequencer selectably coupled to the timing circuitry. The sequencer is configured to coordinate compute operations. The apparatus also includes a source external to the PIM capable device. The sequencer is configured to receive a command instruction set from the source to initiate performance of a compute operation.

Aspects for vector operations in neural network are described herein. The aspects may include a vector caching unit configured to store a first vector and a second vector, wherein the first vector includes one or more first elements and the second vector includes one or more second elements. The aspects may further include one or more adders and a combiner. The one or more adders may be configured to respectively add each of the first elements to a corresponding one of the second elements to generate one or more addition results. The combiner may be configured to combine a combiner configured to combine the one or more addition results into an output vector.

Disclosed are systems and methods related to providing for the optimized software implementations of artificial intelligence (“AI”) networks. The system receives operations (“ops”) consisting of a set of instructions to be performed within an AI network. The system then receives microkernels implementing one or more instructions to be performed within the AI network for a specific hardware component. Next, the system generates a kernel for each of the operations. Generating the kernel for each of the operations includes configuring input data to be received from the AI network; detecting a specific hardware component to be used; selecting one or more microkernels to be invoked by the kernel based on the detection of the specific hardware component; and configuring output data to be sent to the AI network as a result of the invocation of the microkernel(s).

Systems, apparatuses, and methods for compacting multiple groups of micro-operations into individual cache lines of a micro-operation cache are disclosed. A processor includes at least a decode unit and a micro-operation cache. When a new group of micro-operations is decoded and ready to be written to the micro-operation cache, the micro-operation cache determines which set is targeted by the new group of micro-operations. If there is a way in this set that can store the new group without evicting any existing group already stored in the way, then the new group is stored into the way with the existing group(s) of micro-operations. Metadata is then updated to indicate that the new group of micro-operations has been written to the way. Additionally, the micro-operation cache manages eviction and replacement policy at the granularity of micro-operation groups rather than at the granularity of cache lines.