Method and apparatus for scheduling matrix operations in digital processing systems
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United States of America
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Feb 18, 2025
Grant Date -
Nov 26, 2020
app pub date -
May 7, 2020
filing date -
May 7, 2020
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Abstract
Artificial intelligence is an increasingly important sector of the computer industry. However, artificial intelligence is an extremely computationally intensive field such that performing artificial intelligence calculations can be expensive, time consuming, and energy consuming. Fortunately, many of the calculations required for artificial intelligence applications can be performed in parallel such that specialized linear algebra matrix processors can greatly increase computational performance. But even with linear algebra matrix processors; performance can be limited due to complex data dependencies. Without proper coordination, linear algebra matrix processors may end up idle or spending large amounts of time moving data around. Thus, this document discloses methods for efficiently scheduling linear algebra matrix processors.
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