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AutoDSE: Enabling Software Programmers to Design Efficient FPGA Accelerators.

, , , and . ACM Trans. Design Autom. Electr. Syst., 27 (4): 32:1-32:27 (2022)

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AutoAccel: Automated Accelerator Generation and Optimization with Composable, Parallel and Pipeline Architecture., , , and . CoRR, (2018)TensorIR: An Abstraction for Automatic Tensorized Program Optimization., , , , , , , , , and 1 other author(s). CoRR, (2022)From JVM to FPGA: Bridging Abstraction Hierarchy via Optimized Deep Pipelining., , and . HotCloud, USENIX Association, (2018)MOCHA: Multinode Cost Optimization in Heterogeneous Clouds with Accelerators., , , , , , and . FPGA, page 273-279. ACM, (2021)Decoupled Model Schedule for Deep Learning Training., , , , , and . CoRR, (2023)DietCode: Automatic Optimization for Dynamic Tensor Programs., , , , , , , , , and . MLSys, mlsys.org, (2022)TensorIR: An Abstraction for Automatic Tensorized Program Optimization., , , , , , , , , and 1 other author(s). ASPLOS (2), page 804-817. ACM, (2023)RAF: Holistic Compilation for Deep Learning Model Training., , , , , , , , , and 2 other author(s). CoRR, (2023)AutoDSE: Enabling Software Programmers Design Efficient FPGA Accelerators., , , and . FPGA, page 147. ACM, (2021)Bring Your Own Codegen to Deep Learning Compiler., , , , , , , , and . CoRR, (2021)