Sambanova Systems, Inc.

Sitanshu Gupta

Raghu Prabhakar

Sumti Jairath

Matheen Musaddiq

Nathan Francis Sheeley

Ajit Punj

Kin Hing Leung

Yuan Lin

Scott Layson Burson

Matthew Thomas Grimm

Luca Boasso

David Alan Koeplinger

Ruddhi Arun Chaphekar

Pramod Nataraja

Weiwei Chen

Mingran Wang

Maulik Desai

Matthew Vilim

Amitabh Menon

Zhekun Zhang

Ayesha Siddiqua

Apurv Vivek

Abhishek Srivastava

Sankar Rachuru

Darshan Dhimantkumar Gandhi

The technology disclosed relates to storing a dataflow graph with a plurality of compute nodes that transmit data along data connections, and controlling data transmission between compute nodes in the plurality of compute nodes along the data connections by using control connections to control writing of data.

Flow control for reconfigurable processors

A data processing system for implementing operations that generate a dynamically-sized output is presented. The data processing system includes a reconfigurable processor that is configured to implement a first operation, a second operation, a recording unit, and a control unit. The first operation generates an output, wherein a size of the output is unknown during a configuration phase. The second operation receives the output of the first operation as an input. The recording unit generates control data that is indicative of the size of the output. The control unit that provides the control data to the second operation, wherein the second operation processes the input based on the control data.

Dynamically-sized data structures on data flow architectures

A reconfigurable processor comprises an array of processing units and an instrumentation network. The array of processing units is configured to execute runtime events to execute an application. The instrumentation network is operatively coupled to the array of processing units. The instrumentation network comprises a control bus configured to form control signal routes in the instrumentation network. The instrumentation network further comprises a plurality of instrumentation counters having inputs and outputs connected to the control bus and to the processing units. Instrumentation counters in the plurality instrumentation units are configurable to consume control signals on the inputs and produce counts of the runtime events on the outputs.

Instrumentation networks for data flow graphs

A data processing system comprises memory, compile time logic, runtime logic, and instrumentation profiling logic. The memory stores a dataflow graph for an application. The dataflow graph has a plurality of compute nodes that are configured to be producers to produce data for execution of the application, and to be consumers to consume the data for execution of the application. The compile time logic partitions execution of the dataflow graph into stages. Each of the stages has one or more compute nodes, one or more producers, and one or more consumers. The runtime logic determines a processing latency for each of the stages by calculating time elapsed between producers of a particular stage receiving input data and consumers of the particular stage receiving output data. The instrumentation profiling logic generates performance statistics for the dataflow graph based on the processing latency determined for each of the stages.

Compile time instrumentation of data flow graphs

A compiler configured to configure memory nodes with a ready-to-read credit counter and a write credit counter. The ready-to-read credit counter of a particular upstream memory node initialized with as many read credits as a buffer depth of a corresponding downstream memory node. The ready-to-read credit counter configured to decrement when a buffer data unit is written by the particular upstream memory node into the corresponding downstream memory node, and to increment when the particular upstream memory node receives from the corresponding downstream memory node a read ready token. The write credit counter of the particular upstream memory node initialized with one or more write credits and configured to decrement when the particular upstream memory node begins writing the buffer data unit into the corresponding downstream memory node, and to increment when the particular upstream memory node receives from the corresponding downstream memory node a write done token.

Anti-congestion flow control for reconfigurable processors

A method of processing partitions of a tensor in a target order includes receiving, by a reorder unit and from two or more producer units, a plurality of partitions of a tensor in a first order that is different from the target order, storing the plurality of partitions in the reorder unit, and providing, from the reorder unit, the plurality of partitions in the target order to one or more consumer units. In an example, the one or more consumer units process the plurality of partitions in the target order.

Tensor partitioning and partition access order

The technology disclosed relates to matrix multiplication where the multiplier can be a sparse matrix. In particular, a multiplication device includes first circuitry configured to obtain the multiplicand matrix and an index of columns of the multiplier matrix and to generate an intermediate matrix that has one row per entry in the index copied from a respective row of the multiplicand matrix based on a value of a corresponding entry in the index. The device also includes second circuitry configured to receive the intermediate matrix from the first circuitry, obtain non-zero values of the multiplier matrix and a list of a number of non-zero entries per row of the multiplier matrix, and generate a product matrix as a result of multiplies of the non-zero values of the multiplier matrix and the intermediate matrix.

Sparse matrix multiplier in hardware and a reconfigurable data processor including same

An integrated circuit includes a plurality of configurable units, each configurable unit having two or more corresponding sections. The plurality of configurable units is arranged in a serial arrangement to form a chain of sections of the configurable units. A data bus is connected to the plurality of configurable units which communicates data at a clock rate. The chain of sections is to receive and write a series of tensors at the clock rate at a first end section of the chain of sections, and sequentially propagate the series of tensors through individual sections within the chain of sections at the clock rate. The chain of sections is to output the series of tensors at a second end section of the chain of sections. The chain of sections is to also output the series of tensors at an intermediate section of the chain of sections.

Multi-headed multi-buffer for buffering data for processing

A data processing system comprises compile time logic, runtime logic, a control bus, and instrumentation units operatively coupled to processing units of an array. The compile time logic is configured to generate configuration files for a dataflow graph. The runtime logic is configured to execute the configuration files on the array, and to trigger start and stop events, as defined by the configuration files, in response to implementation of compute and memory operations of the dataflow graph on the array. A control bus is configured to form event routes in the array. The instrumentation units have inputs and outputs connected to the control bus and to the processing units. The instrumentation units are configured to consume the start events on the inputs and start counting clock cycles, consume the stop events on the inputs and stop counting the clock cycles, and report the counted clock cycles on the outputs.

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San Jose, CA, United States of America

Sitanshu Gupta