Deep learning implementations using systolic arrays and fused operations

Abstract

Disclosed embodiments relate to deep learning implementations using systolic arrays and fused operations. In one example, a processor includes fetch and decode circuitry to fetch and decode an instruction having fields to specify an opcode and locations of a destination and N source matrices, the opcode indicating the processor is to load the N source matrices from memory, perform N convolutions on the N source matrices to generate N feature maps, and store results of the N convolutions in registers to be passed to an activation layer, wherein the processor is to perform the N convolutions andthe activation layer with at most one memory load of each of the N source matrices. The processor further includes scheduling circuitry to schedule execution of the instruction and execution circuitry to execute the instruction as per the opcode.

Description

technical field [0001] The field of the invention relates generally to computer processor architecture, and more specifically to implementations of deep learning using systolic arrays and fused operations. Background technique [0002] Matrices are increasingly important in many computational tasks such as machine learning and other massive data processing. Deep learning architectures, such as deep neural networks, have been applied to areas including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, and drug design. [0003] Unfortunately, the widespread adoption and application of deep learning-based machine learning systems faces challenges related to computational requirements, power consumption, and memory bandwidth utilization. For example, deep learning neural network models can be many megabytes in size and require millions of arithmetic operations per second to process...

AI Technical Summary

Problems solved by technology

[0003] Unfortunately, the widespread adoption and application of deep learning-based machine learning systems faces challenges related to computational requirements, power consumption, and memory bandwidth utilization

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