{"doi":"10.1088/2634-4386/aceea9","title":"Integration of Ag-CBRAM crossbars and Mott ReLU neurons for efficient implementation of deep neural networks in hardware","abstract":"Abstract In-memory computing with emerging non-volatile memory devices (eNVMs) has shown promising results in accelerating matrix-vector multiplications. However, activation function calculations are still being implemented with general processors or large and complex neuron peripheral circuits. Here, we present the integration of Ag-based conductive bridge random access memory (Ag-CBRAM) crossbar arrays with Mott rectified linear unit (ReLU) activation neurons for scalable, energy and area-efficient hardware (HW) implementation of deep neural networks. We develop Ag-CBRAM devices that can achieve a high ON/OFF ratio and multi-level programmability. Compact and energy-efficient Mott ReLU neuron devices implementing ReLU activation function are directly connected to the columns of Ag-CBRAM crossbars to compute the output from the weighted sum current. We implement convolution filters and activations for VGG-16 using our integrated HW and demonstrate the successful generation of feature maps for CIFAR-10 images in HW. Our approach paves a new way toward building a highly compact and energy-efficient eNVMs-based in-memory computing system.","journal":"Neuromorphic Computing and Engineering","year":2023,"id":367269,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9523,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":752333,"name":"Sangheon Oh","orcid":"0000-0003-2371-7410","position":1,"is_corresponding":false},{"id":1124422,"name":"Jaeseoung Park","orcid":"0000-0002-7680-9709","position":2,"is_corresponding":false},{"id":1124423,"name":"Javier del Valle","orcid":"0000-0002-6210-5321","position":3,"is_corresponding":false},{"id":1124424,"name":"Pavel Salev","orcid":"0000-0002-1171-9219","position":4,"is_corresponding":false},{"id":703326,"name":"Iván K. Schuller","orcid":"0000-0002-9078-7120","position":5,"is_corresponding":false},{"id":397790,"name":"Duygu Kuzum","orcid":"0000-0002-2125-1285","position":6,"is_corresponding":false},{"id":752331,"name":"Yuhan Shi","orcid":"0000-0002-3246-6994","position":0,"is_corresponding":true}],"reference_count":23,"raw_metadata":null,"created_at":"2026-07-19T01:15:12.215513Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}