Wisconsin Alumni Research Foundation

Simplemachines Inc.

Vinay Gangadhar

Karthikeyan Sankaralingam

Anthony Nowatzki

Yunfeng Li

Newsha Ardalani

Preyas Shah

A reconfigurable hardware accelerator for computers combines a high-speed dataflow processor, having programmable functional units rapidly reconfigured in a network of programmable switches, with a stream processor that may autonomously access memory in predefined access patterns after receiving simple stream instructions. The result is a compact, high-speed processor that may exploit parallelism associated with many application-specific programs susceptible to acceleration.

Reconfigurable computer accelerator providing stream processor and dataflow processor

A hardware accelerator for computers combines a stand-alone, high-speed, fixed program dataflow functional element with a stream processor, the latter of which may autonomously access memory in predefined access patterns after receiving simple stream instructions and provide them to the dataflow functional element. The result is a compact, high-speed processor that may exploit fixed program dataflow functional elements.

Computer architecture with fixed program dataflow elements and stream processor

A dataflow accelerator including a control/command core, a scratchpad and a coarse grain reconfigurable array (CGRA) according to an exemplary embodiment is disclosed. The scratchpad may include a write controller to transmit data to an input vector port interface and to receive data from the input vector port interface. The CGRA may receive data from the input vector port interface and includes a plurality of interconnects and a plurality of functional units.

Systems and methods for stream-dataflow acceleration wherein a delay is implemented so as to equalize arrival times of data packets at a destination functional unit

According to exemplary embodiments, a method, processor, and system for accelerating a recurrent neural network are presented. A method of accelerating a recurrent neural network may include distributing from a first master core to each of a plurality of processing cores a same relative one or more columns of weight matrix data for each of a plurality of gates in the neural network, broadcasting a current input vector from the first master core to each of the processing cores, and processing each column of weight matrix data in parallel, at each of the respective processing cores.

Accelerating parallel processing of data in a recurrent neural network

A method for performing acceleration of simultaneous access to shared data may include providing a plurality of groups of cores and a plurality of shared memory structures, providing a pod comprising the plurality of groups of cores linked by a common broadcast channel, and coordinating each shared memory structure to provide a logically unified memory structure. Each memory structure may be associated with a group of cores, and each group of cores may include one or more cores. The common broadcast channel may be operatively coupled to each shared memory structure. The coordinating each shared memory structure may include identifying a simultaneous read-reuse load to a first shared memory structure, fetching data corresponding to the simultaneous read-reuse load, and forwarding the data to shared memory structures other than the first shared memory structure and to groups of cores other than a first group of cores via the broadcast channel.

Method, computer program product, and apparatus for acceleration of simultaneous access to shared data

A dataflow computer processor is teamed with a general computer processor so that program portions of an application program particularly suited to dataflow execution may be transferred to the dataflow processor during portions of the execution of the application program by the general computer processor. During this time the general computer processor may be placed in partial shutdown for energy conservation.

Growing community of inventors

Madison, WI, United States of America

Vinay Gangadhar