{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T12:34:21Z","timestamp":1782390861479,"version":"3.54.5"},"reference-count":31,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,5]]},"DOI":"10.1109\/saci.2018.8440953","type":"proceedings-article","created":{"date-parts":[[2018,8,23]],"date-time":"2018-08-23T22:14:23Z","timestamp":1535062463000},"page":"000425-000430","source":"Crossref","is-referenced-by-count":7,"title":["Embedded Solutions for Deep Neural Networks Implementation"],"prefix":"10.1109","author":[{"given":"Adrian-Aliosa","family":"Erofei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristian-Filip","family":"Druta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catalin","family":"Daniel Caleanu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref31","author":"rosebrock","year":"0","journal-title":"Optimizing OpenCV on the Raspberry Pi"},{"key":"ref30","author":"zeevi","year":"0","journal-title":"Build a faster OpenCV for Raspberry Pi3"},{"key":"ref10","year":"0","journal-title":"Google Tango Augmented Reality Platform"},{"key":"ref11","year":"0","journal-title":"The deep learning hardware Battle"},{"key":"ref12","author":"yoshida","year":"2015","journal-title":"How will Deep Learning Change SoCs?"},{"key":"ref13","article-title":"Texas Instruments","year":"2014","journal-title":"TDA3x SoC Processors for Advanced Driver Assist Systems (ADAS) Technical Brief"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCE.2016.7430652"},{"key":"ref15","article-title":"Cadence","year":"0","journal-title":"Tensilica Vision DSPs for Imaging Computer Vision CNN"},{"key":"ref16","article-title":"Accelerating Compute-Intensive Applications with GPUs and FPGAn","author":"che","year":"2008","journal-title":"Proceedings of Symposium on Application Specific Processors-SASP'08"},{"key":"ref17","article-title":"Altera","year":"0","journal-title":"FPGA Coprocessing Evolution Sustained Performance Approaches Peak Performance” White Paper"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/2847263.2847265"},{"key":"ref19","article-title":"DNNWEAVER: From High-Level Deep Network Models to FPGA Acceleration","author":"sharma","year":"2016","journal-title":"Workshop on Cognitive Architectures (CogArch) in conjunction with ASPLOS"},{"key":"ref28","author":"szegedy","year":"0","journal-title":"Going Deeper with Convolutions"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1561\/2200000006"},{"key":"ref27","author":"jin","year":"0","journal-title":"clCaffe* Unleashing the Power of Intel Graphics for Deep Learning Acceleration"},{"key":"ref3","article-title":"Deep Learning","author":"goodfellow","year":"2016","journal-title":"MIT Press"},{"key":"ref6","year":"0","journal-title":"Intel “Intel Xeon Processor E5–2698 V3"},{"key":"ref29","author":"iandola","year":"2016","journal-title":"SqueezeNet AlexNet-level accuracy with 50x fewer parameters and"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CCBD.2016.029"},{"key":"ref8","article-title":"Intel","year":"0","journal-title":"Discover the Intel Joule Compute Module"},{"key":"ref7","article-title":"NVIDIA","year":"2015","journal-title":"GPU-Based Deep Learning Inference A Performance and Power Analysis"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref9","year":"0","journal-title":"Qualcomm Snapdragon Neural Processing Engine and Zeroth Machine Learning Software Development Kit"},{"key":"ref1","author":"armasu","year":"0","journal-title":"The Rise Of Client-Side Deep Learning"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/FPL.2016.7577314"},{"key":"ref22","article-title":"NVIDIA, “GPU-Based Deep Learning Inference: A Performance and Power Analysis","year":"2015","journal-title":"Whitepaper"},{"key":"ref21","article-title":"NVIDIA","year":"0","journal-title":"Whitepaper NVIDIA Tegra Xl NVIDIA'S New Mobile Superchip” Vl 0"},{"key":"ref24","article-title":"Google","year":"0","journal-title":"Google Supercharges Machine Learning Tasks with TPU Custom Chip"},{"key":"ref23","article-title":"Production Deep Learning with NVIDIA GPU Inference Engine","author":"gray","year":"2016","journal-title":"Posted"},{"key":"ref26","year":"0","journal-title":"Movidius Vision Processing Unit"},{"key":"ref25","article-title":"Nervana","year":"0","journal-title":"The Nervana Engine"}],"event":{"name":"2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI)","location":"Timisoara","start":{"date-parts":[[2018,5,17]]},"end":{"date-parts":[[2018,5,19]]}},"container-title":["2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8412770\/8440916\/08440953.pdf?arnumber=8440953","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T15:15:29Z","timestamp":1643210129000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8440953\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/saci.2018.8440953","relation":{},"subject":[],"published":{"date-parts":[[2018,5]]}}}