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From High-Level Frameworks to custom Silicon with SODA., , , , , , , , , and 3 other author(s). HCS, page 1-13. IEEE, (2022)SODA: a New Synthesis Infrastructure for Agile Hardware Design of Machine Learning Accelerators., , , , , , , and . ICCAD, page 98:1-98:7. IEEE, (2020)GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent., , , , and . CoRR, (2018)Bridging Python to Silicon: The SODA Toolchain., , , , , , , , , and 2 other author(s). IEEE Micro, 42 (5): 78-88 (2022)Physics-informed Machine Learning of Parameterized Fundamental Diagrams., , , , and . CoRR, (2022)Invited: Software Defined Accelerators From Learning Tools Environment., , , , , , and . DAC, page 1-6. IEEE, (2020)Automated Generation of Integrated Digital and Spiking Neuromorphic Machine Learning Accelerators., , , , , , , , , and 3 other author(s). ICCAD, page 1-7. IEEE, (2021)Scaling the training of particle classification on simulated MicroBooNE events to multiple GPUs., , , , and . CoRR, (2020)SO(DA)2: End-to-end Generation of Specialized Reconfigurable Architectures (Invited Talk)., , , , , , , , , and . PARMA-DITAM@HiPEAC, volume 100 of OASIcs, page 1:1-1:15. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2022)What does fault tolerant deep learning need from MPI?, , , and . EuroMPI/USA, page 13:1-13:11. ACM, (2017)