Abstract: Embodiments relate to managing tasks that when executed by a neural processor circuit instantiates a neural network. A neural task manager circuit within the neural processor circuit can switch between tasks in different task queues. Each task queue is configured to store a reference to a task list of tasks for instantiating a neural network. Each task queue can also be assigned a priority parameter. While the neural processor circuit is executing tasks of a first task list and prior to completion of each task, the neural task manager circuit can switch between task queues according to the priority parameters for execution of tasks of a second task list by the neural processor circuit. The neural processor circuit includes one or more neural engine circuits that are configured to perform neural operations by executing the tasks assigned by the task manager.

Abstract: Embodiments relate to managing tasks that when executed by a neural processor circuit instantiates a neural network. A neural task manager circuit within the neural processor circuit can switch between tasks in different task queues. Each task queue is configured to store a reference to a task list of tasks for instantiating a neural network. Each task queue can also be assigned a priority parameter. While the neural processor circuit is executing tasks of a first task list and prior to completion of each task, the neural task manager circuit can switch between task queues according to the priority parameters for execution of tasks of a second task list by the neural processor circuit. The neural processor circuit includes one or more neural engine circuits that are configured to perform neural operations by executing the tasks assigned by the task manager.

Abstract: Embodiments relate to a neural processor circuit with scalable architecture for instantiating one or more neural networks. The neural processor circuit includes a data buffer coupled to a memory external to the neural processor circuit, and a plurality of neural engine circuits. To execute tasks that instantiate the neural networks, each neural engine circuit generates output data using input data and kernel coefficients. A neural processor circuit may include multiple neural engine circuits that are selectively activated or deactivated according to configuration data of the tasks. Furthermore, an electronic device may include multiple neural processor circuits that are selectively activated or deactivated to execute the tasks.

Abstract: Embodiments relate to a neural processor circuit including a plurality of neural engine circuits, a data buffer, and a kernel fetcher circuit. At least one of the neural engine circuits is configured to receive matrix elements of a matrix as at least the portion of the input data from the data buffer over multiple processing cycles. The at least one neural engine circuit further receives vector elements of a vector from the kernel fetcher circuit, wherein each of the vector elements is extracted as a corresponding kernel to the at least one neural engine circuit in each of the processing cycles. The at least one neural engine circuit performs multiplication between the matrix and the vector as a convolution operation to produce at least one output channel of the output data.

Abstract: In an embodiment, a computation engine may perform computations on input vectors having vector elements of a first precision and data type. The computation engine may convert the vector elements from the first precision to a second precision and may also interleave the vector elements as specified by an instruction issued by the processor to the computation engine. The interleave may be based on a ratio of a result precision and the second precision. An extract instruction may be supported to extract results from the computations and convert and deinterleave the vector elements to provide a compact result in a desired order.

Abstract: A method and apparatus provide means for compressing instruction code size. An Instruction Set Architecture (ISA) encodes instructions compact, usual or extended bit lengths. Commonly used instructions are encoded having both compact and usual bit lengths, with compact or usual bit length instructions chosen based on power, performance or code size requirements. Instructions of the ISA can be used in both privileged and non-privileged operating modes of a microprocessor. The instruction encodings can be used interchangeably in software applications. Instructions from the ISA may be executed on any programmable device enabled for the ISA, including a single instruction set architecture processor or a multi-instruction set architecture processor.

Abstract: Embodiments relate to managing tasks that when executed by a neural processor circuit instantiates a neural network. The neural processor circuit includes neural engine circuits and a neural task manager circuit. The neural task manager circuit includes multiple task queues and a task arbiter circuit. Each task queue stores a reference to a task list of tasks for a machine learning operation. Each task queue may be associated with a priority parameter. Based on the priority of the task queues, the task arbiter circuit retrieves configuration data for a task from a memory external to the neural processor circuit, and provides the configuration data to components of the neural processor circuit including the neural engine circuits. The configuration data programs the neural processor circuit to execute the task. For example, the configuration data may include input data and kernel data processed by the neural engine circuits to execute the task.

Abstract: Embodiments relate to a neural processor circuit including a plurality of neural engine circuits, a data buffer, and a kernel fetcher circuit. At least one of the neural engine circuits is configured to receive matrix elements of a matrix as at least the portion of the input data from the data buffer over multiple processing cycles. The at least one neural engine circuit further receives vector elements of a vector from the kernel fetcher circuit, wherein each of the vector elements is extracted as a corresponding kernel to the at least one neural engine circuit in each of the processing cycles. The at least one neural engine circuit performs multiplication between the matrix and the vector as a convolution operation to produce at least one output channel of the output data.

Abstract: Embodiments relate to a neural processor circuit with scalable architecture for instantiating one or more neural networks. The neural processor circuit includes a data buffer coupled to a memory external to the neural processor circuit, and a plurality of neural engine circuits. To execute tasks that instantiate the neural networks, each neural engine circuit generates output data using input data and kernel coefficients. A neural processor circuit may include multiple neural engine circuits that are selectively activated or deactivated according to configuration data of the tasks. Furthermore, an electronic device may include multiple neural processor circuits that are selectively activated or deactivated to execute the tasks.

Abstract: Embodiments relate to managing tasks that when executed by a neural processor circuit instantiates a neural network. A neural task manager circuit within the neural processor circuit can switch between tasks in different task queues. Each task queue is configured to store a reference to a task list of tasks for instantiating a neural network. Each task queue can also be assigned a priority parameter. While the neural processor circuit is executing tasks of a first task list and prior to completion of each task, the neural task manager circuit can switch between task queues according to the priority parameters for execution of tasks of a second task list by the neural processor circuit. The neural processor circuit includes one or more neural engine circuits that are configured to perform neural operations by executing the tasks assigned by the task manager.

Abstract: In an embodiment, a computation engine may perform computations on input vectors having vector elements of a first precision and data type. The computation engine may convert the vector elements from the first precision to a second precision and may also interleave the vector elements as specified by an instruction issued by the processor to the computation engine. The interleave may be based on a ratio of a result precision and the second precision. An extract instruction may be supported to extract results from the computations and convert and deinterleave the vector elements to to provide a compact result in a desired order.

Abstract: A method and apparatus provide means for compressing instruction code size. An Instruction Set Architecture (ISA) encodes instructions compact, usual or extended bit lengths. Commonly used instructions are encoded having both compact and usual bit lengths, with compact or usual bit length instructions chosen based on power, performance or code size requirements. Instructions of the ISA can be used in both privileged and non-privileged operating modes of a microprocessor. The instruction encodings can be used interchangeably in software applications. Instructions from the ISA may be executed on any programmable device enabled for the ISA, including a single instruction set architecture processor or a multi-instruction set architecture processor.

Abstract: A method and apparatus provide means for compressing instruction code size. An Instruction Set Architecture (ISA) encodes instructions compact, usual or extended bit lengths. Commonly used instructions are encoded having both compact and usual bit lengths, with compact or usual bit length instructions chosen based on power, performance or code size requirements. Instructions of the ISA can be used in both privileged and non-privileged operating modes of a microprocessor. The instruction encodings can be used interchangeably in software applications. Instructions from the ISA may be executed on any programmable device enabled for the ISA, including a single instruction set architecture processor or a multi-instruction set architecture processor.

Abstract: A method and apparatus provide means for compressing instruction code size. An Instruction Set Architecture (ISA) encodes instructions compact, usual or extended bit lengths. Commonly used instructions are encoded having both compact and usual bit lengths, with compact or usual bit length instructions chosen based on power, performance or code size requirements. Instructions of the ISA can be used in both privileged and non-privileged operating modes of a microprocessor. The instruction encodings can be used interchangeably in software applications. Instructions from the ISA may be executed on any programmable device enabled for the ISA, including a single instruction set architecture processor or a multi-instruction set architecture processor.

Abstract: A method and apparatus provide means for compressing instruction code size. An Instruction Set Architecture (ISA) encodes instructions compact, usual or extended bit lengths. Commonly used instructions are encoded having both compact and usual bit lengths, with compact or usual bit length instructions chosen based on power, performance or code size requirements. Instructions of the ISA can be used in both privileged and non-privileged operating modes of a microprocessor. The instruction encodings can be used interchangeably in software applications. Instructions from the ISA may be executed on any programmable device enabled for the ISA, including a single instruction set architecture processor or a multi-instruction set architecture processor.

Abstract: A processing system is provided consisting of an interrupt pin, multiple registers, a stack pointer, and an automatic interrupt system. The multiple registers store a number of processor states values. When the system detects an interrupt on the interrupt pin the system prepares to enter an exception mode where the automatic interrupt system causes an interrupt vector to be fetched, the stack pointer to be updated, and the processor state values to be read in parallel from the registers and stored in memory locations based on the updated stack pointer, prior to the execution of an interrupt service routine. A method for automatic hardware interrupt handling is also presented.

Abstract: A processing system is provided consisting of an interrupt pin, multiple registers, a stack pointer, and an automatic interrupt system. The multiple registers store a number of processor states values. When the system detects an interrupt on the interrupt pin the system prepares to enter an exception mode where the automatic interrupt system causes an interrupt vector to be fetched, the stack pointer to be updated, and the processor state values to be read in parallel from the registers and stored in memory locations based on the updated stack pointer, prior to the execution of an interrupt service routine.

Abstract: A re-encoded instruction set architecture (ISA) provides smaller bit-width instructions or a combination of smaller and larger bit-width instructions to improve instruction execution efficiency and reduce code footprint. The ISA can be re-encoded from a legacy ISA having larger bit-width instructions, and the re-encoded ISA can maintain assembly-level compatibility with the ISA from which it is derived. In addition, the re-encoded ISA can have new and different types of additional instructions, including instructions with encoded arguments determined by statistical analysis and instructions that have the effect of combinations of instructions.

Abstract: A method and apparatus provide means for compressing instruction code size. An Instruction Set Architecture (ISA) encodes instructions compact, usual or extended bit lengths. Commonly used instructions are encoded having both compact and usual bit lengths, with compact or usual bit length instructions chosen based on power, performance or code size requirements. Instructions of the ISA can be used in both privileged and non-privileged operating modes of a microprocessor. The instruction encodings can be used interchangeably in software applications. Instructions from the ISA may be executed on any programmable device enabled for the ISA, including a single instruction set architecture processor or a multi-instruction set architecture processor.

Abstract: A real-time, multi-threaded embedded system includes rules for handling traps and interrupts to avoid problems such as priority inversion and re-entrancy. By defining a global interrupt priority value for all active threads and only accepting interrupts having a priority higher than the interrupt priority value, priority inversion can be avoided. Switching to the same thread before any interrupt servicing, and disabling interrupts and thread switching during interrupt servicing can simplify the interrupt handling logic. By storing trap background data for traps and servicing traps only in their originating threads, trap traceability can be preserved. By disabling interrupts and thread switching during trap servicing, unintended trap re-entrancy and servicing disruption can be prevented.