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Some of the medical applications are real-time and may be implemented in embedded devices. In these cases, achieving the highest level of accuracy is not the only concern. Computation runtime and power consumption are also considered as the most important performance indicators. These parameters are mainly evaluated in hardware design phase. In this research, an energy efficient deep learning accelerator for endoscopic images classification (DLA-E) is proposed. This accelerator can be implemented in the future endoscopic imaging equipments for helping medical specialists during endoscopy or colonoscopy in order of making faster and more accurate decisions. The proposed DLA-E consists of 256 processing elements with 1000 bps network on chip bandwidth. Based on the simulation results of this research, the best dataflow for this accelerator based on MobileNet v2 is kcp_ws from the weight stationary (WS) family. Total energy consumption and total runtime of this accelerator on the investigated dataset is 4.56\u2009\u00d7\u200910<jats:sup>9<\/jats:sup> MAC (multiplier\u2013accumulator) energy and 1.73\u2009\u00d7\u200910<jats:sup>7<\/jats:sup> cycles respectively, which is the best result in comparison to other combinations of CNNs and dataflows.<\/jats:p>","DOI":"10.1186\/s40537-023-00775-8","type":"journal-article","created":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T15:02:27Z","timestamp":1685026947000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["DLA-E: a deep learning accelerator for endoscopic images classification"],"prefix":"10.1186","volume":"10","author":[{"given":"Hamidreza","family":"Bolhasani","sequence":"first","affiliation":[]},{"given":"Somayyeh Jafarali","family":"Jassbi","sequence":"additional","affiliation":[]},{"given":"Arash","family":"Sharifi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,25]]},"reference":[{"key":"775_CR1","unstructured":"Das, A.; Rad, P. 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All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript. The following authors have affiliations with organizations with direct or indirect financial interest in the subject matter discussed in the manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"76"}}