Intel Corporation

Movidius Limited

Martin Power

Steven Hsu

Amit Agarwal

Kevin Brady

Martin-Thomas Grymel

Niall Hanrahan

Gary Baugh

Cormac Brick

David Bernard

David T Bernard

Brendan Barry

Arnab Raha

Vasile Toma, Ii

Conor Byrne

Deepak Abraham Mathaikutty

Raymond Jit-Hung Sung

David Thomas Bernard

Methods, apparatus, systems, and articles of manufacture are disclosed to debug a hardware accelerator such as a neural network accelerator for executing Artificial Intelligence computational workloads. An example apparatus includes a core with a core input and a core output to execute executable code based on a machine-learning model to generate a data output based on a data input, and debug circuitry coupled to the core. The debug circuitry is configured to detect a breakpoint associated with the machine-learning model, compile executable code based on at least one of the machine-learning model or the breakpoint. In response to the triggering of the breakpoint, the debug circuitry is to stop the execution of the executable code and output data such as the data input, data output and the breakpoint for debugging the hardware accelerator.

Systems, apparatus, and methods to debug accelerator hardware

Methods, apparatus, systems and articles of manufacture are disclosed for sparse tensor storage for neural network accelerators. An example apparatus includes sparsity map generating circuitry to generate a sparsity map corresponding to a tensor, the sparsity map to indicate whether a data point of the tensor is zero, static storage controlling circuitry to divide the tensor into one or more storage elements, and a compressor to perform a first compression of the one or more storage elements to generate one or more compressed storage elements, the first compression to remove zero points of the one or more storage elements based on the sparsity map and perform a second compression of the one or more compressed storage elements, the second compression to store the one or more compressed storage elements contiguously in memory.

Methods and apparatus for sparse tensor storage for neural network accelerators

Methods, apparatus, systems and articles of manufacture are disclosed to perform machine-learning model operations on sparse accelerators. An example apparatus includes first circuitry, second circuitry to generate sparsity data based on an acceleration operation, and third circuitry to instruct one or more data buffers to provide at least one of activation data or weight data based on the sparsity data to the first circuitry, the first circuitry to execute the acceleration operation based on the at least one of the activation data or the weight data.

Methods and apparatus to perform machine-learning model operations on sparse accelerators

Methods, apparatus, systems, and articles of manufacture are disclosed that increase data reuse for multiply and accumulate (MAC) operations. An example apparatus includes a MAC circuit to process a first context of a set of a first type of contexts stored in a first buffer and a first context of a set of a second type of contexts stored in a second buffer. The example apparatus also includes control logic circuitry to, in response to determining that there is an additional context of the second type to be processed in the set of the second type of contexts, maintain the first context of the first type in the first buffer. The control logic circuitry is also to, in response to determining that there is an additional context of the first type to be processed in the set of the first type of contexts maintain the first context of the second type in the second buffer and iterate a pointer of the second buffer from a first position to a next position in the second buffer.

Methods, apparatus, and articles of manufacture to increase data reuse for multiply and accumulate (MAC) operations

Methods, systems, apparatus and articles of manufacture to identify features within an image are disclosed herein. An example apparatus includes a horizontal cost (HCOST) engine to apply a first row of pixels of a macroblock to an input of a first HCOST unit, the first HCOST unit including a number of difference calculators; and a difference calculator engine to apply corresponding rows of pixels of a search window of a source image to corresponding ones of the number of difference calculators of the first HCOST unit, the corresponding ones of the number of difference calculators to calculate respective sums of absolute difference (SAD) values between (a) the first row of pixels of the macroblock and (b) the corresponding rows of pixels of the search window.

Methods, systems, apparatus, and articles of manufacture to identify features within an image

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Dublin, Ireland

Martin Power