Abstract:
An example apparatus to perform a convolution on an input tensor includes a parameters generator to: generate a horizontal hardware execution parameter for a horizontal dimension of the input tensor based on a kernel parameter and a layer parameter; and generate a vertical hardware execution parameter for a vertical dimension of the input tensor based on the kernel parameter and the layer parameter; an accelerator interface to configure a hardware accelerator circuitry based on the horizontal and vertical hardware execution parameters; a horizontal Iterator controller to determine when the hardware accelerator circuitry completes the first horizontal iteration of the convolution; and a vertical Iterator controller to determine when the hardware accelerator circuitry completes the first vertical iteration of the convolution.
Abstract:
An example apparatus to perform a convolution on an input tensor includes a parameters generator to: generate a horizontal hardware execution parameter for a horizontal dimension of the input tensor based on a kernel parameter and a layer parameter; and generate a vertical hardware execution parameter for a vertical dimension of the input tensor based on the kernel parameter and the layer parameter; an accelerator interface to configure a hardware accelerator circuitry based on the horizontal and vertical hardware execution parameters; a horizontal Iterator controller to determine when the hardware accelerator circuitry completes the first horizontal iteration of the convolution; and a vertical Iterator controller to determine when the hardware accelerator circuitry completes the first vertical iteration of the convolution.
Abstract:
A processor includes a decode unit to decode a packed finite impulse response (FIR) filter instruction that indicates one or more source packed data operands, a plurality of FIR filter coefficients, and a destination storage location. The source operand(s) include a first number of data elements and a second number of additional data elements. The second number is one less than a number of FIR filter taps. An execution unit, in response to the packed FIR filter instruction being decoded, is to store a result packed data operand. The result packed data operand includes the first number of FIR filtered data elements that each is to be based on a combination of products of the plurality of FIR filter coefficients and a different corresponding set of data elements from the one or more source packed data operands, which is equal in number to the number of FIR filter taps.
Abstract:
Systems and methods may provide for establishing an out-of-band (OOB) channel between a local wireless interface and a remote backend receiver, and receiving information from a peripheral device via the local wireless interface. Additionally, the information may be sent to the backend receiver via the OOB channel, wherein the OOB channel bypasses a local operating system. In one example, a secure Bluetooth stack is used to receive the information from the peripheral device.
Abstract:
Methods, apparatus, systems and articles of manufacture are disclosed to configure heterogenous components in an accelerator. An example apparatus includes a graph compiler to identify a workload node in a workload and generate a selector for the workload node, and the selector to identify an input condition and an output condition of a compute building block, wherein the graph compiler is to, in response to obtaining the identified input condition and output condition from the selector, map the workload node to the compute building block.
Abstract:
An example apparatus to perform a convolution on an input tensor includes a parameters generator to: generate a horizontal hardware execution parameter for a horizontal dimension of the input tensor based on a kernel parameter and a layer parameter; and generate a vertical hardware execution parameter for a vertical dimension of the input tensor based on the kernel parameter and the layer parameter; an accelerator interface to configure a hardware accelerator circuitry based on the horizontal and vertical hardware execution parameters; a horizontal Iterator controller to determine when the hardware accelerator circuitry completes the first horizontal iteration of the convolution; and a vertical Iterator controller to determine when the hardware accelerator circuitry completes the first vertical iteration of the convolution.
Abstract:
Methods, apparatus, systems and articles of manufacture are disclosed to configure heterogenous components in an accelerator. An example apparatus includes a graph compiler to identify a workload node in a workload and generate a selector for the workload node, and the selector to identify an input condition and an output condition of a compute building block, wherein the graph compiler is to, in response to obtaining the identified input condition and output condition from the selector, map the workload node to the compute building block.
Abstract:
A processor includes a decode unit to decode a packed finite impulse response (FIR) filter instruction that indicates one or more source packed data operands, a plurality of FIR filter coefficients, and a destination storage location. The source operand(s) include a first number of data elements and a second number of additional data elements. The second number is one less than a number of FIR filter taps. An execution unit, in response to the packed FIR filter instruction being decoded, is to store a result packed data operand. The result packed data operand includes the first number of FIR filtered data elements that each is to be based on a combination of products of the plurality of FIR filter coefficients and a different corresponding set of data elements from the one or more source packed data operands, which is equal in number to the number of FIR filter taps.
Abstract:
An example apparatus to perform a convolution on an input tensor includes a parameters generator to: generate a horizontal hardware execution parameter for a horizontal dimension of the input tensor based on a kernel parameter and a layer parameter; and generate a vertical hardware execution parameter for a vertical dimension of the input tensor based on the kernel parameter and the layer parameter; an accelerator interface to configure a hardware accelerator circuitry based on the horizontal and vertical hardware execution parameters; a horizontal Iterator controller to determine when the hardware accelerator circuitry completes the first horizontal iteration of the convolution; and a vertical Iterator controller to determine when the hardware accelerator circuitry completes the first vertical iteration of the convolution.
Abstract:
Methods, apparatus, systems and articles of manufacture are disclosed that enable out-of-order pipelined execution of static mapping of a workload to one or more computational building blocks of an accelerator. An example apparatus includes an interface to load a first number of credits into memory; a comparator to compare the first number of credits to a threshold number of credits associated with memory availability in a buffer; and a dispatcher to, when the first number of credits meets the threshold number of credits, select a workload node of the workload to be executed at a first one of the one or more computational building blocks.