@article{dey_baker_schabel_li_franzon_2021, title={A Scalable Cluster-based Hierarchical Hardware Accelerator for a Cortically Inspired Algorithm}, volume={17}, ISSN={["1550-4840"]}, DOI={10.1145/3447777}, number={4}, journal={ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS}, author={Dey, Sumon and Baker, Lee and Schabel, Joshua and Li, Weifu and Franzon, Paul D.}, year={2021}, month={Oct} }
 @article{franzon_davis_rotenberg_stevens_lipa_nigussie_pan_baker_schabel_dey_et al._2021, title={Design for 3D Stacked Circuits}, ISSN={["2380-9248"]}, DOI={10.1109/IEDM19574.2021.9720553}, journal={2021 IEEE INTERNATIONAL ELECTRON DEVICES MEETING (IEDM)}, author={Franzon, P. and Davis, W. and Rotenberg, E. and Stevens, J. and Lipa, S. and Nigussie, T. and Pan, H. and Baker, L. and Schabel, J. and Dey, S. and et al.}, year={2021} }
 @article{baker_patti_franzon_2021, title={Multi-ANN embedded system based on a custom 3D-DRAM}, ISSN={["2164-0157"]}, DOI={10.1109/3DIC52383.2021.9687617}, journal={2021 IEEE INTERNATIONAL 3D SYSTEMS INTEGRATION CONFERENCE (3DIC)}, author={Baker, Lee B. and Patti, Robert and Franzon, Paul}, year={2021} }
 @article{park_baker_franzon_2019, title={Appliance Identification Algorithm for a Non-Intrusive Home Energy Monitor Using Cogent Confabulation}, volume={10}, ISSN={1949-3053}, DOI={10.1109/TSG.2017.2751465}, abstractNote={This paper presents an appliance identification algorithm for use with a non-intrusive home energy monitor based on a cogent confabulation neural network. As a cogent confabulation neural network does not require multiplications during the identification phase, it is an effective choice for systems with low-computational capability. A non-intrusive home energy monitor needs to learn not only the energy patterns of individual appliances but also those of combinations of appliances. To relieve the burden of learning power patterns of the combinations, this paper proposes a parameter-building scheme based on the parameters of individual appliances. The proposed algorithm is evaluated on datasets prepared by the reference energy disaggregation dataset and the authors. The average success rate was 83.8% for up to eight appliances and showed better performance than the combinatorial optimization and artificial neural network approaches.}, number={1}, journal={IEEE Transactions on Smart Grid}, author={Park, S. W. and Baker, L. B. and Franzon, P. D.}, year={2019}, month={Jan}, pages={714–721} }
 @inproceedings{schabel_baker_dey_li_franzon, title={Processor-in-memory support for artificial neural networks}, booktitle={2016 IEEE International Conference on Rebooting Computing (icrc)}, author={Schabel, J. and Baker, L. and Dey, S. and Li, W. F. and Franzon, P. D.} }