# Probabilistic AI ## expand\_more ## Select Publications Download 3 citations download- [BibTeX](/bibcite/export?pager_style=no_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_peoplepublications%5D&taxonomy_filters%5Bfield_hwp_c_project123456%5D%5B0%5D%5Btarget_id%5D=172615&&&format=bibtex) - [EndNote X3 XML](/bibcite/export?pager_style=no_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_peoplepublications%5D&taxonomy_filters%5Bfield_hwp_c_project123456%5D%5B0%5D%5Btarget_id%5D=172615&&&format=endnote8) - [EndNote 7 XML](/bibcite/export?pager_style=no_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_peoplepublications%5D&taxonomy_filters%5Bfield_hwp_c_project123456%5D%5B0%5D%5Btarget_id%5D=172615&&&format=endnote7) - [Endnote tagged](/bibcite/export?pager_style=no_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_peoplepublications%5D&taxonomy_filters%5Bfield_hwp_c_project123456%5D%5B0%5D%5Btarget_id%5D=172615&&&format=tagged) - [Marc](/bibcite/export?pager_style=no_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_peoplepublications%5D&taxonomy_filters%5Bfield_hwp_c_project123456%5D%5B0%5D%5Btarget_id%5D=172615&&&format=marc) - [PubMedId](/bibcite/export?pager_style=no_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_peoplepublications%5D&taxonomy_filters%5Bfield_hwp_c_project123456%5D%5B0%5D%5Btarget_id%5D=172615&&&format=pubmed_id) - [RIS](/bibcite/export?pager_style=no_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_peoplepublications%5D&taxonomy_filters%5Bfield_hwp_c_project123456%5D%5B0%5D%5Btarget_id%5D=172615&&&format=ris) ### 2019 Glenn Ko, Yuji Chai, Rob Rutenbar, David Brooks, and Gu Wei. 2019. “[Accelerating Bayesian Inference on Structured Graphs Using Parallel Gibbs Sampling](/publications/accelerating-bayesian-inference-structured-graphs-using-parallel-gibbs)” Glenn Ko, Yuji Chai, Rob Rutenbar, David Brooks, and Gu Wei. 2019. “[Accelerating Bayesian Inference on Structured Graphs Using Parallel Gibbs Sampling](/publications/accelerating-bayesian-inference-structured-graphs-using-parallel-gibbs)” - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://doi.org/10.1109/FPL.2019.00033) - [ picture\_as\_pdfAccelerating Bayesian Inf...](/sites/g/files/omnuum11281/files/vlsiarch/files/m24.fpl2019-slides-acceleratingbayesianinference.pdf) Bayesian models and inference is a class of machine learning that is useful for solving problems where the amount of data is scarce and prior knowledge about the application allows you to draw better conclusions. However, Bayesian models often requires... - [ descriptionPublisher's Version](https://doi.org/10.1109/FPL.2019.00033) - [ picture\_as\_pdfAccelerating Bayesian Inf...](/sites/g/files/omnuum11281/files/vlsiarch/files/m24.fpl2019-slides-acceleratingbayesianinference.pdf) Ko, Yuji Chai, Rutenbar, David Brooks, and Gu Wei. 2019. “[Flexgibbs: Reconfigurable Parallel Gibbs Sampling Accelerator for Structured Graphs](/publications/flexgibbs-reconfigurable-parallel-gibbs-sampling-accelerator-structured-graphs)”. In 2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), Pp. 334–334 Ko, Yuji Chai, Rutenbar, David Brooks, and Gu Wei. 2019. “[Flexgibbs: Reconfigurable Parallel Gibbs Sampling Accelerator for Structured Graphs](/publications/flexgibbs-reconfigurable-parallel-gibbs-sampling-accelerator-structured-graphs)”. In 2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), Pp. 334–334 - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://doi.org/10.1109/FCCM.2019.00075) - [ picture\_as\_pdfFlexgibbs: Reconfigurable...](/sites/g/files/omnuum11281/files/vlsiarch/files/flexgibbs_reconfigurable_parallel_gibbs_sampling_accelerator_for_structured_graphs.pdf) Many consider one of the key components to the success of deep learning as its compatibility with existing accelerators, mainly GPU. While GPUs are great at handling linear algebra kernels commonly found in deep learning, they are not the optimal... - [ descriptionPublisher's Version](https://doi.org/10.1109/FCCM.2019.00075) - [ picture\_as\_pdfFlexgibbs: Reconfigurable...](/sites/g/files/omnuum11281/files/vlsiarch/files/flexgibbs_reconfigurable_parallel_gibbs_sampling_accelerator_for_structured_graphs.pdf) ### 2018 Brandon Reagen, Udit Gupta, Robert Adolf, Michael Mitzenmacher, Alexander Rush, Gu Wei, and David Brooks. 2018. “[Weightless: Lossy Weight Encoding For Deep Neural Network Compression](/publications/weightless-lossy-weight-encoding-deep-neural-network-compression)”. In International Conference on Machine Learning, Pp. 4324–4333 Brandon Reagen, Udit Gupta, Robert Adolf, Michael Mitzenmacher, Alexander Rush, Gu Wei, and David Brooks. 2018. “[Weightless: Lossy Weight Encoding For Deep Neural Network Compression](/publications/weightless-lossy-weight-encoding-deep-neural-network-compression)”. In International Conference on Machine Learning, Pp. 4324–4333 - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://arxiv.org/abs/1711.04686) - [ picture\_as\_pdfWeightless: Lossy Weight ...](/sites/g/files/omnuum11281/files/vlsiarch/files/1711.04686.pdf) The large memory requirements of deep neural networks limit their deployment and adoption on many devices. Model compression methods effectively reduce the memory requirements of these models, usually through applying transformations such as weight... - [ descriptionPublisher's Version](https://arxiv.org/abs/1711.04686) - [ picture\_as\_pdfWeightless: Lossy Weight ...](/sites/g/files/omnuum11281/files/vlsiarch/files/1711.04686.pdf) [ See all project publications arrow\_circle\_right ](https://prod-vlsiarch.drupalsites.harvard.edu/publications?f%5B0%5D=bibcite_reference_hwp_c_project123456%3A172615)