SRAM-based in-memory computing for machine learning applications

In memory computing has become popular recently. It not only could accelerate the AI application on hardware, but also could solve the Neumann problem. In this field, digital SRAM design for machine learning has received a lot of attention due to its easy design and high accuracy characteristics. In...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Zhao, Yuqin
مؤلفون آخرون: Kim Tae Hyoung
التنسيق: Thesis-Master by Coursework
اللغة:English
منشور في: Nanyang Technological University 2022
الموضوعات:
الوصول للمادة أونلاين:https://hdl.handle.net/10356/156761
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الوصف
الملخص:In memory computing has become popular recently. It not only could accelerate the AI application on hardware, but also could solve the Neumann problem. In this field, digital SRAM design for machine learning has received a lot of attention due to its easy design and high accuracy characteristics. In this paper, a 4k weight-selective digital SRAM design is implemented with improvements on Bitcell and Adder Tree. It uses TG logic in the design to improve the speed and eliminate power consumption. The weight-Selective function is used to adapt to the different complexity of the calculation. The simulation is done by using the TSMC65LP process. The Bitcell Array is 64x64, GOPS is 409.6 and the frequency is 200MHz.