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Scale-out acceleration for machine learning., , , , , and . MICRO, page 367-381. ACM, (2017)Axilog: Abstractions for Approximate Hardware Design and Reuse., , , , , , , , , and . IEEE Micro, 35 (5): 16-30 (2015)Axilog: language support for approximate hardware design., , , , , , , , , and 2 other author(s). DATE, page 812-817. ACM, (2015)Memristor based adders., , and . ACSSC, page 1256-1260. IEEE, (2014)Balancing Generality and Specialization for Machine Learning in the Post-ISA Era.. Georgia Institute of Technology, Atlanta, GA, USA, (2019)base-search.net (ftgeorgiatech:oai:smartech.gatech.edu:1853/61075).AxGames: Towards Crowdsourcing Quality Target Determination in Approximate Computing., , , , and . ASPLOS, page 623-636. ACM, (2016)TABLA: A unified template-based framework for accelerating statistical machine learning., , , , , , and . HPCA, page 14-26. IEEE Computer Society, (2016)From high-level deep neural models to FPGAs., , , , , , , and . MICRO, page 1-12. IEEE Computer Society, (2016)Improving the energy efficiency of relational and NoSQL databases via query optimizations., , and . SUSCOM, (2019)RoboX: An End-to-End Solution to Accelerate Autonomous Control in Robotics., , , and . ISCA, page 479-490. IEEE Computer Society, (2018)