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MOCHA: Multinode Cost Optimization in Heterogeneous Clouds with Accelerators.

, , , , , , and . FPGA, page 273-279. ACM, (2021)

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Overcoming Data Transfer Bottlenecks in DNN Accelerators via Layer-Conscious Memory Managment., , , , and . FPGA, page 120. ACM, (2019)Efficiently Programming Large Language Models using SGLang., , , , , , , , , and 2 other author(s). CoRR, (2023)AutoDSE: Enabling Software Programmers to Design Efficient FPGA Accelerators., , , and . ACM Trans. Design Autom. Electr. Syst., 27 (4): 32:1-32:27 (2022)Grape: Practical and Efficient Graphed Execution for Dynamic Deep Neural Networks on GPUs., , , , , , and . MICRO, page 1364-1380. ACM, (2023)Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs., , , , , and . CoRR, (2022)AutoDSE: Enabling Software Programmers Design Efficient FPGA Accelerators., , , and . CoRR, (2020)The SMEM Seeding Acceleration for DNA Sequence Alignment., , , , , and . FCCM, page 32-39. IEEE Computer Society, (2016)Latte: Locality Aware Transformation for High-Level Synthesis., , , and . FCCM, page 125-128. IEEE Computer Society, (2018)Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs., , , , , and . ASPLOS (2), page 370-384. ACM, (2023)AutoAccel: Automated Accelerator Generation and Optimization with Composable, Parallel and Pipeline Architecture., , , and . CoRR, (2018)