Machine Learning Hardware_Considerations and Accelerator Approaches.pdf

1、ISSCC 2024Short CourseMachine Learning Hardware:Considerations and Accelerator Approaches 2024 IEEE International Solid-State Circuits ConferenceIntroduction to Machine Learning Applications andHardware-Aware OptimizationsRangharajan VenkatesanNVIDIA CorporationFebruary 2024ISSCC 2024 Short CourseCo

2、ntact Infoemail:Machine learning hardware:considerations and accelerator approaches1 of 89 2024 IEEE International Solid-State Circuits ConferenceOutlineIntroduction to machine learning and deep neural networksTrends and challenges in hardware designApproaches to scaling single-chip performanceQuant

3、izationSparsityScaling beyond single chip with package-level integrationEfficient communication architectureExploiting parallelismISSCC 2024 Short CourseMachine learning hardware:considerations and accelerator approaches2 of 89 2024 IEEE International Solid-State Circuits ConferenceArtificial Intell

4、igence(AI)Artificial Intelligence:“The science and engineering of creating intelligent machines”-John McCarthy,1956ISSCC 2024 Short CourseArtificial IntelligenceMachine learning hardware:considerations and accelerator approaches3 of 89 2024 IEEE International Solid-State Circuits ConferenceMachine L

5、earning(ML)Machine Learning:“Field of study that gives computers the ability to learn without being explicitly programmed.”-Arthur Samuel,1959ISSCC 2024 Short CourseArtificial IntelligenceMachine LearningMachine learning hardware:considerations and accelerator approaches4 of 89 2024 IEEE Internation

6、al Solid-State Circuits ConferenceDeep learning(aka Deep neural networks)Deep Learning:“Seek to exploit the unknown structure in the input distribution in order to discover good representations,often at multiple levels.”-Yoshua Bengio,2012ISSCC 2024 Short CourseArtificial IntelligenceMachine Learnin