{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T09:34:15Z","timestamp":1743154455687,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030983468"},{"type":"electronic","value":"9783030983475"}],"license":[{"start":{"date-parts":[[2012,2,24]],"date-time":"2012-02-24T00:00:00Z","timestamp":1330041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2012,2,24]],"date-time":"2012-02-24T00:00:00Z","timestamp":1330041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-98347-5_17","type":"book-chapter","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T14:04:15Z","timestamp":1661177055000},"page":"429-450","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enabling Efficient Inference of Convolutional Neural Networks via Approximation"],"prefix":"10.1007","author":[{"given":"Georgios","family":"Zervakis","sequence":"first","affiliation":[]},{"given":"Iraklis","family":"Anagnostopoulos","sequence":"additional","affiliation":[]},{"given":"Hussam","family":"Amrouch","sequence":"additional","affiliation":[]},{"given":"J\u00f6rg","family":"Henkel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2012,2,24]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Amrouch H, Khaleghi B, Gerstlauer A, Henkel J. Towards aging-induced approximations. In: Design automation conference. 2017.","DOI":"10.1145\/3061639.3062331"},{"issue":"11","key":"17_CR2","doi-asserted-by":"publisher","first-page":"1647","DOI":"10.1109\/TC.2019.2916869","volume":"68","author":"H Amrouch","year":"2019","unstructured":"Amrouch H, Ehsani SB, Gerstlauer A, Henkel J. On the efficiency of voltage overscaling under temperature and aging effects. IEEE Trans Comput. 2019;68(11):1647\u201362.","journal-title":"IEEE Trans Comput"},{"issue":"9","key":"17_CR3","doi-asserted-by":"publisher","first-page":"3127","DOI":"10.1109\/TCSI.2020.2990672","volume":"67","author":"H Amrouch","year":"2020","unstructured":"Amrouch H, Pahwa G, Gaidhane AD, Dabhi CK, Klemme F, Prakash O, Chauhan YS. Impact of variability on processor performance in negative capacitance finfet technology. IEEE Trans Circuits Syst I Reg Pap. 2020;67(9):3127\u201337.","journal-title":"IEEE Trans Circuits Syst I Reg Pap"},{"issue":"11","key":"17_CR4","doi-asserted-by":"publisher","first-page":"3842","DOI":"10.1109\/TCAD.2020.3012753","volume":"39","author":"H Amrouch","year":"2020","unstructured":"Amrouch H, Zervakis G, Salamin S, Kattan H, Anagnostopoulos I, Henkel J. Npu thermal management. IEEE Trans Comput Aided Des Integr Circuits Syst. 2020;39(11):3842\u201355. https:\/\/doi.org\/10.1109\/TCAD.2020.3012753.","journal-title":"IEEE Trans Comput Aided Des Integr Circuits Syst"},{"issue":"2","key":"17_CR5","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1109\/TVLSI.2019.2940943","volume":"28","author":"MS Ansari","year":"2019","unstructured":"Ansari MS, Mrazek V, Cockburn BF, Sekanina L, Vasicek Z, Han J. Improving the accuracy and hardware efficiency of neural networks using approximate multipliers. IEEE Trans Very Large Scale Integr (VLSI) Syst 2019;28(2):317\u201328.","journal-title":"IEEE Trans Very Large Scale Integr (VLSI) Syst"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L, Kai Li, Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In: Conf. on Comp. Vis. and Pat. Recogn. (CVPR). 2009. pp. 248\u201355.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"17_CR7","unstructured":"Han S, Pool J, Tran J, Dally WJ. Learning both weights and connections for efficient neural networks. Preprint. arXiv:150602626. 2015."},{"issue":"4","key":"17_CR8","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1109\/MDAT.2021.3069952","volume":"38","author":"C Hao","year":"2021","unstructured":"Hao C, Dotzel J, Xiong J, Benini L, Zhang Z, Chen D (2021) Enabling design methodologies and future trends for edge ai: Specialization and codesign. IEEE Design Test 38(4):7\u201326. https:\/\/doi.org\/10.1109\/MDAT.2021.3069952.","journal-title":"IEEE Design Test"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Jacob B, Kligys S, Chen B, Zhu M, Tang M, Howard A, Adam H, Kalenichenko D. Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. pp. 2704\u20132713.","DOI":"10.1109\/CVPR.2018.00286"},{"key":"17_CR10","unstructured":"Jouppi NP, et al. In-datacenter performance analysis of a tensor processing unit. In: Int. Symp. on computer architecture. 2017. pp 1\u201312."},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Karmarkar N, Karp RM. An efficient approximation scheme for the one-dimensional bin-packing problem. In: 23rd Annual symposium on foundations of computer science (SFCS 1982). IEEE; 1982. pp. 312\u201320.","DOI":"10.1109\/SFCS.1982.61"},{"issue":"4","key":"17_CR12","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/MM.2021.3061335","volume":"41","author":"H Kattan","year":"2021","unstructured":"Kattan H, Chung SW, Henkel J, Amrouch H. On-demand mobile cpu cooling with thin-film thermoelectric array. IEEE Micro. 2021;41(4):67\u201373.","journal-title":"IEEE Micro"},{"issue":"6","key":"17_CR13","doi-asserted-by":"publisher","first-page":"2569","DOI":"10.1109\/TCSI.2021.3069664","volume":"68","author":"F Klemme","year":"2021","unstructured":"Klemme F, Amrouch H. Machine learning for on-the-fly reliability-aware cell library characterization. IEEE Trans Circ Syst I Reg Pap. 2021;68(6):2569\u201379.","journal-title":"IEEE Trans Circ Syst I Reg Pap"},{"issue":"4","key":"17_CR14","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1109\/MM.2018.043191124","volume":"38","author":"V Leon","year":"2018","unstructured":"Leon V, Zervakis G, Xydis S, Soudris D, Pekmestzi K (2018) Walking through the energy-error pareto frontier of approximate multipliers. IEEE Micro. 38(4):40\u20139.","journal-title":"IEEE Micro."},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Li C, Luo W, Sapatnekar SS, Hu J. Joint precision optimization and high level synthesis for approximate computing. In: Proceedings of the 52nd annual design automation conference. 2015. pp. 1\u20136.","DOI":"10.1145\/2744769.2744863"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Mrazek V, Sarwar SS, Sekanina L, Vasicek Z, Roy K. Design of power-efficient approximate multipliers for approximate artificial neural networks. In: Int. Conf. computer-aided design. 2016. pp. 1\u20137.","DOI":"10.1145\/2966986.2967021"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Mrazek V, Hrbacek R, Vasicek Z, Sekanina L. Evoapproxsb: Library of approximate adders and multipliers for circuit design and benchmarking of approximation methods. In: Design, automation & test in Europe conference & exhibition. 2017. pp. 258\u2013261.","DOI":"10.23919\/DATE.2017.7926993"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Mrazek V, Vasicek Z, Sekanina L, Hanif MA, Shafique M. ALWANN: automatic layer-wise approximation of deep neural network accelerators without retraining. In: Int. Conf. computer-aided design. 2019. pp. 1\u20138.","DOI":"10.1109\/ICCAD45719.2019.8942068"},{"issue":"4","key":"17_CR19","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1109\/JETCAS.2020.3032495","volume":"10","author":"V Mrazek","year":"2020","unstructured":"Mrazek V, Sekanina L, Vasicek Z. Libraries of approximate circuits: Automated design and application in CNN accelerators. IEEE J Emerg Sel Top Circuits Syst. 2020;10(4):406\u201318.","journal-title":"IEEE J Emerg Sel Top Circuits Syst"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Park JS, Jang JW, Lee H, Lee D, Lee S, Jung H, Lee S, Kwon S, Jeong K, Song JH, et al. 9.5 a 6k-mac feature-map-sparsity-aware neural processing unit in 5nm flagship mobile soc. In: IEEE international solid-state circuits conference (ISSCC), vol. 64. 2021. pp. 152\u20134.","DOI":"10.1109\/ISSCC42613.2021.9365928"},{"key":"17_CR21","unstructured":"Renda A, Frankle J, Carbin M. Comparing rewinding and fine-tuning in neural network pruning. Preprint. arXiv:200302389. 2020."},{"key":"17_CR22","doi-asserted-by":"publisher","unstructured":"Salamin S, Zervakis G, Spantidi O, Anagnostopoulos I, Henkel J, Amrouch H. Reliability-aware quantization for anti-aging npus. In: Design, automation & test in Europe conference & exhibition. 2021. pp. 1460\u201365. https:\/\/doi.org\/10.23919\/DATE51398.2021.9474094.","DOI":"10.23919\/DATE51398.2021.9474094"},{"issue":"2","key":"17_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3097264","volume":"14","author":"SS Sarwar","year":"2018","unstructured":"Sarwar SS, Venkataramani S, Ankit A, Raghunathan A, Roy K. Energy-efficient neural computing with approximate multipliers. ACM J Emerg Technol Comput Syst (JETC). 2018;14(2):1\u201323.","journal-title":"ACM J Emerg Technol Comput Syst (JETC)"},{"key":"17_CR24","doi-asserted-by":"crossref","unstructured":"Shafique M, Ahmad W, Hafiz R, Henkel J. A low latency generic accuracy configurable adder. In: 2015 52nd ACM\/EDAC\/IEEE design automation conference (DAC). 2015. pp 1\u20136. https:\/\/doi.org\/10.1145\/2744769.2744778.","DOI":"10.1145\/2744769.2744778"},{"key":"17_CR25","doi-asserted-by":"publisher","unstructured":"Spantidi O, Zervakis G, Anagnostopoulos I, Amrouch H, Henkel J. Positive\/negative approximate multipliers for DNN accelerators. In: IEEE\/ACM International Conference On Computer Aided Design (ICCAD). 2021. pp. 1\u20139. https:\/\/doi.org\/10.1109\/ICCAD51958.2021.9643491.","DOI":"10.1109\/ICCAD51958.2021.9643491"},{"issue":"12","key":"17_CR26","doi-asserted-by":"publisher","first-page":"4670","DOI":"10.1109\/TCSI.2020.3019460","volume":"67","author":"Z Tasoulas","year":"2020","unstructured":"Tasoulas ZG, Zervakis G, Anagnostopoulos I, Amrouch H, Henkel J (2020) Weight-oriented approximation for energy-efficient neural network inference accelerators. IEEE Trans Circuits Syst I Reg Pap. 67(12):4670\u201383. https:\/\/doi.org\/10.1109\/TCSI.2020.3019460.","journal-title":"IEEE Trans Circuits Syst I Reg Pap."},{"key":"17_CR27","doi-asserted-by":"crossref","unstructured":"Vaverka F, Mrazek V, Vasicek Z, Sekanina L, Hanif MA, Shafique M. Tfapprox: Towards a fast emulation of DNN approximate hardware accelerators on GPU. In: Design, automation and test in Europe conference (DATE). 2020. p. 4.","DOI":"10.23919\/DATE48585.2020.9116299"},{"issue":"12","key":"17_CR28","doi-asserted-by":"publisher","first-page":"2232","DOI":"10.1109\/JPROC.2020.3029453","volume":"108","author":"S Venkataramani","year":"2020","unstructured":"Venkataramani S, et al. Efficient ai system design with cross-layer approximate computing. Proc IEEE. 2020;108(12):2232\u201350. https:\/\/doi.org\/10.1109\/JPROC.2020.3029453.","journal-title":"Proc IEEE"},{"key":"17_CR29","doi-asserted-by":"crossref","unstructured":"Yang J, Shen X, Xing J, Tian X, Li H, Deng B, Huang J, Hua X. Quantization networks. In: Conf. on Comp. Vis. and Pat. Recogn. (CVPR). 2019. pp. 7300\u20138.","DOI":"10.1109\/CVPR.2019.00748"},{"issue":"10","key":"17_CR30","doi-asserted-by":"publisher","first-page":"3105","DOI":"10.1109\/TVLSI.2016.2535398","volume":"24","author":"G Zervakis","year":"2016","unstructured":"Zervakis G, Tsoumanis K, Xydis S, Soudris D, Pekmestzi K. Design-efficient approximate multiplication circuits through partial product perforation. IEEE Trans Very Large Scale Integr (VLSI) Syst. 2016;24(10):3105\u201317.","journal-title":"IEEE Trans Very Large Scale Integr (VLSI) Syst"},{"issue":"6","key":"17_CR31","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.1109\/TVLSI.2019.2900160","volume":"27","author":"G Zervakis","year":"2019","unstructured":"Zervakis G, Koliogeorgi K, Anagnostos D, Zompakis N, Siozios K. Vader: Voltage-driven netlist pruning for cross-layer approximate arithmetic circuits. IEEE Trans Very Large Scale Integr (VLSI) Syst. 2019;27(6):1460\u201364. https:\/\/doi.org\/10.1109\/TVLSI.2019.2900160.","journal-title":"IEEE Trans Very Large Scale Integr (VLSI) Syst"},{"issue":"4","key":"17_CR32","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1109\/TCSII.2018.2869025","volume":"66","author":"G Zervakis","year":"2019","unstructured":"Zervakis G, Xydis S, Soudris D, Pekmestzi K. Multi-level approximate accelerator synthesis under voltage island constraints. IEEE Trans Circ Syst II Express Briefs. 2019;66(4):607\u201311. https:\/\/doi.org\/10.1109\/TCSII.2018.2869025.","journal-title":"IEEE Trans Circ Syst II Express Briefs"},{"key":"17_CR33","doi-asserted-by":"publisher","first-page":"53522","DOI":"10.1109\/ACCESS.2020.2981395","volume":"8","author":"G Zervakis","year":"2020","unstructured":"Zervakis G, Amrouch H, Henkel J. Design automation of approximate circuits with runtime reconfigurable accuracy. IEEE Access. 2020;8:53522\u201338.","journal-title":"IEEE Access."},{"key":"17_CR34","doi-asserted-by":"crossref","unstructured":"Zervakis G, Saadat H, Amrouch H, Gerstlauer A, Parameswaran S, Henkel J. Approximate computing for ML: state-of-the-art, challenges and visions. In: Asia and South Pacific design automation conference. 2021. pp. 189\u201396.","DOI":"10.1145\/3394885.3431632"},{"key":"17_CR35","doi-asserted-by":"publisher","unstructured":"Zervakis G, Spantidi O, Anagnostopoulos I, Amrouch H, Henkel J. Control variate approximation for dnn accelerators. In: 58th ACM\/IEEE Design Automation Conference (DAC). 2021. pp. 481\u2013486. https:\/\/doi.org\/10.1109\/DAC18074.2021.9586092.","DOI":"10.1109\/DAC18074.2021.9586092"}],"container-title":["Approximate Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-98347-5_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T14:15:25Z","timestamp":1661177725000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-98347-5_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,2,24]]},"ISBN":["9783030983468","9783030983475"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-98347-5_17","relation":{},"subject":[],"published":{"date-parts":[[2012,2,24]]},"assertion":[{"value":"24 February 2012","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}