Via Technologies, Inc.

Ip-first, LLC

Via Alliance Semiconductor Co., Ltd.

Dell USA L.p.

Integrated Device Technology, Inc.

I.p.-first, L.l.c.

Other

Shanghai Zhaoxin Semiconductor Co., Ltd.

Terry J Parks

G Glenn Henry

Rodney E Hooker

Thomas A Crispin

Brent Bean

Gerard M Col

Darius D Gaskins

Douglas Raye Reed

Colin Eddy

Glenn Henry

Craig S Jones

Kyle T O'Brien

David S Register

Kenneth L Jeffries

Timothy A Elliott

Michael L Longwell

Arturo Martin-de-Nicolas

Keith D Matteson

Thomas C McDonald

Stephan Gaskins

John D Bunda

Dinesh K Jain

John Michael Greer

Charles P Zeller

Thomas H Holman, Jr

Joseph W Bell

Matthew Daniel Day

Tom Elmer

James R Lundberg

Geoffrey B Hoese

Albert J Loper, Jr

Michael D Durkin

Bryan Wayne Pogor

Charles John Holthaus

Viswanath Mohan

Joseph M Maurin

Esther Parks

E Alan Davis

A method for operating an apparatus that includes a program memory, a data memory and a status register that holds a status, wherein the status has fields including: a program memory address at which a most recent instruction is fetched from the program memory, a data memory access address at which data has most recently been accessed in the data memory by the apparatus and a repeat count indicating a number of times an operation specified in a current program instruction remains to be performed, the apparatus further including a condition register having condition fields corresponding to the status fields held in the status register, the method including: writing the condition register with a condition including the condition fields; and generating an interrupt request to a processing core in response to detecting that the status held in the status register satisfies the condition specified in the condition register.

Neural network unit that interrupts processing core upon condition

A programmable apparatus includes a program memory that holds instructions of a program fetched and executed by the apparatus, a data memory that holds data processed by the instructions, a status register that holds a status having fields: a program memory address at which a most recent instruction is fetched from the program memory, a data memory access address at which data has most recently been accessed in the data memory by the apparatus and a repeat count that indicates a number of times an operation specified in a current program instruction remains to be performed. A condition register has condition fields corresponding to the status register fields. Control logic generates an interrupt request to a processing core in response to detecting that the status held in the status register satisfies the condition specified in the condition register.

A processor including a memory controller for interfacing an external memory and a programmable functional unit (PFU). The PFU is programmed by a PFU program to modify operation of the memory controller, in which the PFU includes programmable logic elements and programmable interconnectors. For example, the PFU is programmed by the PFU program to add a function or otherwise to modify an existing function of the memory controller enhance its functionality during operation of the processor. In this manner, the functionality and/or operation of the memory controller is not fixed once the processor is manufactured, but instead the memory controller may be modified after manufacture to improve efficiency and/or enhance performance of the processor, such as when executing a corresponding process.

Processor with memory controller including dynamically programmable functional unit

A neural network unit includes a register programmable with a control value, a plurality of neural processing units (NPU), and a plurality of activation function units (AFU). Each NPU includes an arithmetic logic unit (ALU) that performs arithmetic and logical operations on a sequence of operands to generate a sequence of results and an accumulator into which the ALU accumulates the sequence of results as an accumulated value. Each AFU includes a first module that performs a first function on the accumulated value to generate a first output, a second module that performs a second function on the accumulated value to generate a second output, the first function is distinct from the second function, and a multiplexer that receives the first and second outputs and selects one of the two outputs based on the control value programmed into the register.

Neural network unit with plurality of selectable output functions

A first data storage holds cache lines, an accelerator has a second data storage that selectively holds accelerator data and cache lines evicted from the first data storage, a tag directory holds tags for cache lines stored in the first and second data storages, and a mode indicator indicates whether the second data storage is operating in a first or second mode in which it respectively holds cache lines evicted from the first data storage or accelerator data. In response to a request to evict a cache line from the first data storage, in the first mode the control logic writes the cache line to the second data storage and updates a tag in the tag directory to indicate the cache line is present in the second data storage, and in the second mode the control logic instead writes the cache line to a system memory.

Processor with selective data storage (of accelerator) operable as either victim cache data storage or accelerator memory and having victim cache tags in lower level cache wherein evicted cache line is stored in said data storage when said data storage is in a first mode and said cache line is stored in system memory rather then said data store when said data storage is in a second mode

A neural network unit has a first memory that holds elements of a data matrix and a second memory that holds elements of a convolution kernel. An array of neural processing units (NPU) each have a multiplexed register that receives a corresponding element of a row from the first memory and that also receives the multiplexed register output of an adjacent NPU. A register receives a corresponding element of a row from the second memory. An arithmetic unit receives the outputs of the register, the multiplexed register and an accumulator and performs a multiply-accumulate operation on them. For each sub-matrix of a plurality of sub-matrices of the data matrix, each arithmetic unit selectively receives either the element from the first memory or the adjacent NPU multiplexed register output and performs a series of the multiply-accumulate operations to accumulate into the accumulator a convolution of the sub-matrix with the convolution kernel.

Neural network unit that performs convolutions using collective shift register among array of neural processing units

A processor with an expandable instruction set architecture for dynamically configuring execution resources. The processor includes a programmable execution unit (PEU) that may be programmed to perform a user-defined function in response to a user-defined instruction (UDI). The PEU includes programmable logic elements and programmable interconnectors that are collectively programmed to perform at least one processing operation. A UDI loader is responsive to a UDI load instruction that specifies a UDI and a location of programming information that is used to program the PEU. The PEU may be programmed for one or more UDIs for one or more processes. An instruction table stores each UDI and corresponding information to identify the UDI and possibly to reprogram the PEU if necessary. A UDI handler consults the instruction table to identify a received UDI and to send corresponding information to the PEU to execute the corresponding user-defined function.

Processor with an expandable instruction set architecture for dynamically configuring execution resources

A microprocessor includes a plurality of processing cores and a configuration register configured to indicate whether each of the plurality of processing cores is enabled or disabled. Each enabled one of the plurality of processing cores is configured to read the configuration register in a first instance to determine which of the plurality of processing cores is enabled or disabled and generate a respective configuration-related value based on the read of the configuration register in the first instance. The configuration register is updated to indicate that a previously enabled one of the plurality of processing cores is disabled. Each enabled one of the plurality of processing cores is configured to read the configuration register in a second instance to determine which of the plurality of processing cores is enabled or disabled and generate the respective configuration-related value based on the read of the configuration register in the second instance.

Dynamic reconfiguration of multi-core processor

A processor includes a front-end portion that issues instructions to execution units that execute the issued instructions. A hardware neural network unit (NNU) execution unit includes a first memory that holds data words associated with artificial neural networks (ANN) neuron outputs, a second memory that holds weight words associated with connections between ANN neurons, and a third memory that holds a program comprising NNU instructions that are distinct, with respect to their instruction set, from the instructions issued to the NNU by the front-end portion of the processor. The program performs ANN-associated computations on the data and weight words. A first instruction instructs the NNU to transfer NNU instructions of the program from architectural general purpose registers to the third memory. A second instruction instructs the NNU to invoke the program stored in the third memory.

Processor with hybrid coprocessor/execution unit neural network unit

A neural network unit has at least one RAM, an output buffer and an array of neural processing units that: read first time step context layer node values from the output buffer; read second time step input layer node values from the RAM; generate second time step hidden layer node values based on the read input and context layer node values; output the hidden layer node values to the output buffer rather than to the RAM; read the hidden layer node values from the output buffer; generate second time step context layer node values based on the read hidden layer node values; output the context layer node values to the output buffer rather than to the RAM; generate output layer node values using the hidden layer node values; write the output layer node values to the RAM; and repeat for a sequence of time steps.

Neural network unit with output buffer feedback for performing recurrent neural network computations

A neural network unit includes a programmable indicator, a first memory that holds first operands, a second memory that holds second operands, neural processing units (NPU), and activation units. Each NPU has an accumulator and an arithmetic unit that performs a series of multiply operations on pairs of the first and second operands received from the first and second memories to generate a series of products, and a series of addition operations on the series of products to accumulate an accumulated value in the accumulator. The activation units perform activation functions on the accumulated values in the accumulators to generate results. When the indicator specifies the first action, the neural network unit writes to the first memory the results generated by the activation units. When the indicator specifies the second action, the neural network unit writes to the first memory the accumulated values in the accumulators.

Neural processing unit that selectively writes back to neural memory either activation function output or accumulator value

An array of N processing units (PU) each has: an accumulator; an arithmetic unit performs an operation on first, second and third inputs to generate a result to store in the accumulator, the first input receives the accumulator output; a weight input is received by the second input to the arithmetic unit; a multiplexed register has first and second data inputs, an output received by the third input to the arithmetic unit, and a control input that controls the data input selection. The multiplexed register output is also received by an adjacent PU's multiplexed register second data input. The N PU's multiplexed registers collectively operate as an N-word rotater when the control input specifies the second data input. Respective first/second memories hold W/D rows of N weight/data words and provide the N weight/data words to the corresponding weight/multiplexed register first data inputs of the N PUs.

Neural network unit with neural memory and array of neural processing units that collectively shift row of data received from neural memory

A processor has functional units that fetch and decode architectural instructions of an architectural instruction set at a first rate, a register that stores a value of an indicator programmable by execution of an architectural instruction of the architectural instruction set, and an execution unit. The execution unit includes a first memory that holds data, a second memory that holds instructions of a program, and a plurality of processing units that execute the program instructions at a second rate to perform operations on data received from the first memory to generate results to be written to the first memory. The instructions are of an instruction set that is distinct from the architectural instruction set. The second rate is the first rate when the indicator is programmed with a first value and the second rate is less than the first rate when the indicator is programmed with a second value.

Processor with variable rate execution unit

A processor includes first and second processing cores configured to support first and second respective subsets of features of its instruction set architecture (ISA) feature set. The first subset is less than all the features of the ISA feature set. The first and second subsets are different but their union is all the features of the ISA feature set. The first core detects a thread, while being executed by the first core rather than by the second core, attempted to employ a feature not in the first subset and, in response, to indicate a switch from the first core to the second core to execute the thread. The unsupported feature may be an unsupported instruction or operating mode. A switch may also be made if the lower performance/power core is being over-utilized or the higher performance/power core is being under-utilized.

Asymmetric multi-core processor with native switching mechanism

An array of N processing units (PU) each has: an accumulator; an arithmetic unit performs an operation on first, second and third inputs to generate a result to store in the accumulator, the first input receives the accumulator output; a weight input is received by the second input to the arithmetic unit; a multiplexed register has first and second data inputs, an output received by the third input to the arithmetic unit, and a control input that controls the data input selection. The multiplexed register output is also received by an adjacent PU's multiplexed register second data input. The N PU's multiplexed registers collectively operate as an N-word rotater when the control input specifies the second data input. Respective first/second memories hold W/D rows of N weight/data words and provide the N weight/data words to the corresponding weight/multiplexed register first data inputs. A sequencer controls the multiplexer and memories.

Neural network unit with neural memory and array of neural processing units and sequencer that collectively shift row of data received from neural memory

Growing community of inventors

Round Rock, TX, United States of America

Terry J Parks