Tianqi Chen On Linkedin Asplos23 Finally, we build an end to end framework on top of our abstraction to automatically optimize deep learning models for given tensor computation primitives. experimental results show that tensorir compilation automatically uses the tensor computation primitives for given hardware backends and delivers performance that is competitive to state of. View a pdf of the paper titled tensorir: an abstraction for automatic tensorized program optimization, by siyuan feng and 10 other authors. deploying deep learning models on various devices has become an important topic. the wave of hardware specialization brings a diverse set of acceleration primitives for multi dimensional tensor computations.
Asplos 23 Session 5c Tensorir An Abstraction For Automatic Tensorir generalizes the loop nest representation used in existing machine learning compilers to bring tensor computation as the first class citizen. finally, we build an end to end framework on top of our abstraction to automatically optimize deep learning models for given tensor computation primitives. Experimental results show that tensorir compilation automatically uses the tensor computation primitives for given hardware backends and delivers performance that is competitive to state of art. Experimental results show that tensorir compilation automatically uses the tensor computation primitives for given hardware backends and delivers performance that is competitive to state of art hand optimized systems across platforms. deploying deep learning models on various devices has become an important topic. the wave of hardware specialization brings a diverse set of acceleration. Figure 1: trends of hardware specialization. the classical acceleration technique uses vector units to process multiple scalar computations simultaneously, which is still widely used on cpu platforms. however, to cater to increasingly heavier computation throughput requirements, modern accelerators usually contain specialized high dimensional tensor computation instructions, creating the need.
Tensorir An Abstraction For Automatic Tensorized Program Optimizati Experimental results show that tensorir compilation automatically uses the tensor computation primitives for given hardware backends and delivers performance that is competitive to state of art hand optimized systems across platforms. deploying deep learning models on various devices has become an important topic. the wave of hardware specialization brings a diverse set of acceleration. Figure 1: trends of hardware specialization. the classical acceleration technique uses vector units to process multiple scalar computations simultaneously, which is still widely used on cpu platforms. however, to cater to increasingly heavier computation throughput requirements, modern accelerators usually contain specialized high dimensional tensor computation instructions, creating the need. Asplos'23: the 28th international conference on architectural support for programming languages and operating systemssession 5c: machine learningsession cha. Tensorir: an abstraction for automatic tensorized program optimization. january 2023. doi: 10.1145 3575693.3576933. conference: asplos '23: 28th acm international conference on architectural.
Tvm Tensorir жµ жћђ зџґд ћ Asplos'23: the 28th international conference on architectural support for programming languages and operating systemssession 5c: machine learningsession cha. Tensorir: an abstraction for automatic tensorized program optimization. january 2023. doi: 10.1145 3575693.3576933. conference: asplos '23: 28th acm international conference on architectural.
Tensorir 系列 一 背景与简介 知乎
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