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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Title: Benchmark framework for 2-D/3-D integrated compute-in-memory based machine learning accelerator
Committee:
Dr. Shimeng Yu, ECE, Chair , Advisor
Dr. Sung-Kyu Lim, ECE
Dr. Saibal Mukhopadhyay, ECE
Dr. Muhannad Bakir, ECE
Dr. Michael Niemier, U of Notre Dame
Abstract: Neural-inspired compute-in-memory (CIM) accelerators with emerging non-volatile memory (eNVM) devices such as resistive random access memory (RRAM) have been proven in silicon for deep learning acceleration. We proposed an end-to-end benchmark framework for the software and hardware evaluation of CIM accelerators with versatile device technologies called DNN+NeuroSim. The proposed framework can support both inference and training chip evaluation, with wide range of technology parameters, from 130nm down to 7nm. Furthermore, as the 3-D integration was proposed as a promising solution to support high bandwidth and on-chip storage for machine learning platforms, we proposed the 3D+NeuroSim with extend 3-D featured parameters and integrated thermal model, to evaluate the monolithic and heterogeneous 3-D integrated CIM accelerators. We have done comprehensive benchmarks across different 2-D and 3-D integrated CIM accelerators and versatile device technologies to explore various design options, and released the proposed frameworks as public tools for research community.
