Abstract: Methods, apparatus, systems and articles of manufacture are disclosed that enable out-of-order pipelined execution of static mapping of a workload to one or more computational building blocks of an accelerator. An example apparatus includes an interface to load a first number of credits into memory; a comparator to compare the first number of credits to a threshold number of credits associated with memory availability in a buffer; and a dispatcher to, when the first number of credits meets the threshold number of credits, select a workload node of the workload to be executed at a first one of the one or more computational building blocks.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to configure heterogenous components in an accelerator. An example apparatus includes a graph compiler to identify a workload node in a workload and generate a selector for the workload node, and the selector to identify an input condition and an output condition of a compute building block, wherein the graph compiler is to, in response to obtaining the identified input condition and output condition from the selector, map the workload node to the compute building block.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to configure heterogenous components in an accelerator. An example apparatus includes a graph compiler to identify a workload node in a workload and generate a selector for the workload node, and the selector to identify an input condition and an output condition of a compute building block, wherein the graph compiler is to, in response to obtaining the identified input condition and output condition from the selector, map the workload node to the compute building block.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed that enable out-of-order pipelined execution of static mapping of a workload to one or more computational building blocks of an accelerator. An example apparatus includes an interface to load a first number of credits into memory; a comparator to compare the first number of credits to a threshold number of credits associated with memory availability in a buffer; and a dispatcher to, when the first number of credits meets the threshold number of credits, select a workload node of the workload to be executed at a first one of the one or more computational building blocks.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to manage memory allocation. An example apparatus includes a memory detector to scan a platform for available memory. The example apparatus also includes a memory size checker to retrieve a virtual memory layout associated with the available memory devices associated with the platform and to determine whether virtual address boundaries of respective ones of a available memory device generate a virtual address gap therebetween. The example apparatus also includes a address assigner to reassign virtual addresses of at least one of the respective ones of the available memory devices to remove the virtual address gap.

Abstract: Systems, apparatuses and methods may provide for technology that determines runtime memory requirements of an artificial intelligence (AI) application, defines a remote address range for a plurality of memories based on the runtime memory requirements, wherein each memory in the plurality of memories corresponds to a processor in a plurality of processors, and defines a shared address range for the plurality of memories based on the runtime memory requirements, wherein the shared address range is aliased. In one example, the technology configures memory mapping hardware to access the remote address range in a linear sequence and access the shared address range in a hashed sequence.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed that enable out-of-order pipelined execution of static mapping of a workload to one or more computational building blocks of an accelerator. An example apparatus includes an interface to load a first number of credits into memory; a comparator to compare the first number of credits to a threshold number of credits associated with memory availability in a buffer; and a dispatcher to, when the first number of credits meets the threshold number of credits, select a workload node of the workload to be executed at a first one of the one or more computational building blocks.

Abstract: Methods and apparatus to implement multiple inference compute engines are disclosed herein. A disclosed example apparatus includes a first inference compute engine, a second inference compute engine, and an accelerator on coherent fabric to couple the first inference compute engine and the second inference compute engine to a converged coherency fabric of a system-on-chip, the accelerator on coherent fabric to arbitrate requests from the first inference compute engine and the second inference compute engine to utilize a single in-die interconnect port.

Abstract: Methods and apparatus to dynamically throttle compute engines are disclosed. A disclosed example apparatus includes one or more compute engines to perform calculations, where the one or more compute engines are to cause a total power request to be issued based on the calculations. The example apparatus also includes a power management unit to receive the total power request and respond to the total power request. The apparatus also includes a throttle manager to adjust a throttle speed of at least one of the one or more compute engines based on comparing a minimum of the power request and a granted power to a total used power of the one or more compute engines prior to the power management unit responding to the total power request.

Abstract: An apparatus includes a communication processor to receive configuration information from a producing compute building block; a credit generator to generate a number of credits for the producing compute building block corresponding to the configuration information, the configuration information including characteristics of a buffer; a source identifier to analyze a returned credit to determine whether the returned credit originates from the producing compute building block or a consuming compute building block; and a duplicator to, when the returned credit originates from the producing compute building block, multiply the returned credit by a first factor, the first factor indicative of a number of consuming compute building blocks identified in the configuration information.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to configure heterogenous components in an accelerator. An example apparatus includes a graph compiler to identify a workload node in a workload and generate a selector for the workload node, and the selector to identify an input condition and an output condition of a compute building block, wherein the graph compiler is to, in response to obtaining the identified input condition and output condition from the selector, map the workload node to the compute building block.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to manage memory allocation. An example apparatus includes a memory detector to scan a platform for available memory. The example apparatus also includes a memory size checker to retrieve a virtual memory layout associated with the available memory devices associated with the platform and to determine whether virtual address boundaries of respective ones of a available memory device generate a virtual address gap therebetween. The example apparatus also includes a address assigner to reassign virtual addresses of at least one of the respective ones of the available memory devices to remove the virtual address gap.

Abstract: Methods and apparatus to implement multiple inference compute engines are disclosed herein. A disclosed example apparatus includes a first inference compute engine, a second inference compute engine, and an accelerator on coherent fabric to couple the first inference compute engine and the second inference compute engine to a converged coherency fabric of a system-on-chip, the accelerator on coherent fabric to arbitrate requests from the first inference compute engine and the second inference compute engine to utilize a single in-die interconnect port.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed that enable dynamic processing of a predefined workload to one or more computational building blocks of an accelerator. An example apparatus includes an interface to obtain a workload node, the workload node associated with a first amount of data, the workload node to be executed at a first one of the one or more computational building blocks; an analyzer to: determine whether the workload node is a candidate for early termination; and in response to determining that the workload node is a candidate for early termination, set a flag associated with a tile of the first amount of data; and a dispatcher to, in response to the tile being transmitted from the first one of the one or more computational building blocks to a buffer, stop execution of the workload node.

Abstract: Methods and apparatus to dynamically throttle compute engines are disclosed. A disclosed example apparatus includes one or more compute engines to perform calculations, where the one or more compute engines are to cause a total power request to be issued based on the calculations. The example apparatus also includes a power management unit to receive the total power request and respond to the total power request. The apparatus also includes a throttle manager to adjust a throttle speed of at least one of the one or more compute engines based on comparing a minimum of the power request and a granted power to a total used power of the one or more compute engines prior to the power management unit responding to the total power request.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed that enable out-of-order pipelined execution of static mapping of a workload to one or more computational building blocks of an accelerator. An example apparatus includes an interface to load a first number of credits into memory; a comparator to compare the first number of credits to a threshold number of credits associated with memory availability in a buffer; and a dispatcher to, when the first number of credits meets the threshold number of credits, select a workload node of the workload to be executed at a first one of the one or more computational building blocks.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.