G06F2015/768

HETEROGENEOUS ACCELERATOR FOR HIGHLY EFFICIENT LEARNING SYSTEMS
20200042477 · 2020-02-06 ·

An apparatus may include a heterogeneous computing environment that may be controlled, at least in part, by a task scheduler in which the heterogeneous computing environment may include a processing unit having fixed logical circuits configured to execute instructions; a reprogrammable processing unit having reprogrammable logical circuits configured to execute instructions that include instructions to control processing-in-memory functionality; and a stack of high-bandwidth memory dies in which each may be configured to store data and to provide processing-in-memory functionality controllable by the reprogrammable processing unit such that the reprogrammable processing unit is at least partially stacked with the high-bandwidth memory dies. The task scheduler may be configured to schedule computational tasks between the processing unit, and the reprogrammable processing unit.

Reconfigurable computing cluster with assets closely coupled at the physical layer by means of an optical circuit switch

Reconfigurable computing clusters, compute nodes within reconfigurable computing clusters, and methods of operating a reconfigurable computing cluster are disclosed. A reconfigurable computing cluster includes an optical circuit switch, and a plurality of computing assets, each of the plurality of computing assets connected to the optical circuit switch by two or more bidirectional fiber optic communications paths.

Heterogeneous accelerator for highly efficient learning systems

An apparatus may include a heterogeneous computing environment that may be controlled, at least in part, by a task scheduler in which the heterogeneous computing environment may include a processing unit having fixed logical circuits configured to execute instructions; a reprogrammable processing unit having reprogrammable logical circuits configured to execute instructions that include instructions to control processing-in-memory functionality; and a stack of high-bandwidth memory dies in which each may be configured to store data and to provide processing-in-memory functionality controllable by the reprogrammable processing unit such that the reprogrammable processing unit is at least partially stacked with the high-bandwidth memory dies. The task scheduler may be configured to schedule computational tasks between the processing unit, and the reprogrammable processing unit.

Heterogeneous accelerator for highly efficient learning systems

An apparatus may include a heterogeneous computing environment that may be controlled, at least in part, by a task scheduler in which the heterogeneous computing environment may include a processing unit having fixed logical circuits configured to execute instructions; a reprogrammable processing unit having reprogrammable logical circuits configured to execute instructions that include instructions to control processing-in-memory functionality; and a stack of high-bandwidth memory dies in which each may be configured to store data and to provide processing-in-memory functionality controllable by the reprogrammable processing unit such that the reprogrammable processing unit is at least partially stacked with the high-bandwidth memory dies. The task scheduler may be configured to schedule computational tasks between the processing unit, and the reprogrammable processing unit.

Reconfigurable microprocessor hardware architecture
10445099 · 2019-10-15 ·

A reconfigurable, multi-core processor includes a plurality of memory blocks and programmable elements, including units for processing, memory interface, and on-chip cognitive data routing, all interconnected by a self-routing cognitive on-chip network. In embodiments, the processing units perform intrinsic operations in any order, and the self-routing network forms interconnections that allow the sequence of operations to be varied and both synchronous and asynchronous data to be transmitted as needed. A method for programming the processor includes partitioning an application into modules, determining whether the modules execute in series, program-driven parallel, or data-driven parallel, determining the data flow required between the modules, assigning hardware resources as needed, and automatically generating machine code for each module. In embodiments, Time Fields are added to the instruction format for all programming units that specify the number of clock cycles for which only one fetched and decoded instruction will be executed.

Reconfigurable processor and operation method therefor

Provided are a reconfigurable processor and a method of operating the same, the reconfigurable processor including: a configurable memory configured to receive a task execution instruction from a control processor; and a plurality of reconfigurable arrays, each configured to receive configuration information from the configurable memory, wherein each of the plurality of reconfigurable arrays simultaneously executes a task based on the configuration information.

Method and system for high performance real time pattern recognition

Systems and methods supporting high performance real time pattern recognition by including time and regional multiplexing using high bandwidth, board-to-board communications channels, and 3D vertical integration. An array of processing boards can each be coupled a rear transition board, the array achieving time and regional multiplexing using high bandwidth board-to-board communications channels and 3D vertical integration.

RECONFIGURABLE COMPUTING CLUSTER WITH ASSETS CLOSELY COUPLED AT THE PHYSICAL LAYER BY MEANS OF AN OPTICAL CIRCUIT SWITCH

Reconfigurable computing clusters, compute nodes within reconfigurable computing clusters, and methods of operating a reconfigurable computing cluster are disclosed. A reconfigurable computing cluster includes an optical circuit switch, and a plurality of computing assets, each of the plurality of computing assets connected to the optical circuit switch by two or more bidirectional fiber optic communications paths.

HETEROGENEOUS ACCELERATOR FOR HIGHLY EFFICIENT LEARNING SYSTEMS
20240193111 · 2024-06-13 ·

An apparatus may include a heterogeneous computing environment that may be controlled, at least in part, by a task scheduler in which the heterogeneous computing environment may include a processing unit having fixed logical circuits configured to execute instructions; a reprogrammable processing unit having reprogrammable logical circuits configured to execute instructions that include instructions to control processing-in-memory functionality; and a stack of high-bandwidth memory dies in which each may be configured to store data and to provide processing-in-memory functionality controllable by the reprogrammable processing unit such that the reprogrammable processing unit is at least partially stacked with the high-bandwidth memory dies. The task scheduler may be configured to schedule computational tasks between the processing unit, and the reprogrammable processing unit.

HETEROGENEOUS ACCELERATOR FOR HIGHLY EFFICIENT LEARNING SYSTEMS
20190079886 · 2019-03-14 ·

An apparatus may include a heterogeneous computing environment that may be controlled, at least in part, by a task scheduler in which the heterogeneous computing environment may include a processing unit having fixed logical circuits configured to execute instructions; a reprogrammable processing unit having reprogrammable logical circuits configured to execute instructions that include instructions to control processing-in-memory functionality; and a stack of high-bandwidth memory dies in which each may be configured to store data and to provide processing-in-memory functionality controllable by the reprogrammable processing unit such that the reprogrammable processing unit is at least partially stacked with the high-bandwidth memory dies. The task scheduler may be configured to schedule computational tasks between the processing unit, and the reprogrammable processing unit.