001024854 001__ 1024854
001024854 005__ 20250203103116.0
001024854 0247_ $$2doi$$a10.48550/ARXIV.2304.04640
001024854 037__ $$aFZJ-2024-02520
001024854 1001_ $$0P:(DE-HGF)0$$aYik, Jason$$b0
001024854 245__ $$aNeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
001024854 260__ $$barXiv$$c2023
001024854 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1714563671_3375
001024854 3367_ $$2ORCID$$aWORKING_PAPER
001024854 3367_ $$028$$2EndNote$$aElectronic Article
001024854 3367_ $$2DRIVER$$apreprint
001024854 3367_ $$2BibTeX$$aARTICLE
001024854 3367_ $$2DataCite$$aOutput Types/Working Paper
001024854 520__ $$aNeuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of nearly 100 co-authors across over 50 institutions in industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we present initial performance baselines across various model architectures on the algorithm track and outline the system track benchmark tasks and guidelines. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community.
001024854 536__ $$0G:(DE-HGF)POF4-5234$$a5234 - Emerging NC Architectures (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001024854 588__ $$aDataset connected to DataCite
001024854 650_7 $$2Other$$aArtificial Intelligence (cs.AI)
001024854 650_7 $$2Other$$aFOS: Computer and information sciences
001024854 7001_ $$0P:(DE-HGF)0$$aBerghe, Korneel Van den$$b1
001024854 7001_ $$0P:(DE-HGF)0$$aBlanken, Douwe den$$b2
001024854 7001_ $$0P:(DE-Juel1)176778$$aBouhadjar, Younes$$b3
001024854 7001_ $$0P:(DE-Juel1)201205$$aFabre, Maxime$$b4
001024854 7001_ $$aHueber, Paul$$b5
001024854 7001_ $$aKleyko, Denis$$b6
001024854 7001_ $$aPacik-Nelson, Noah$$b7
001024854 7001_ $$aSun, Pao-Sheng Vincent$$b8
001024854 7001_ $$aTang, Guangzhi$$b9
001024854 7001_ $$aWang, Shenqi$$b10
001024854 7001_ $$aZhou, Biyan$$b11
001024854 7001_ $$0P:(DE-Juel1)194363$$aAhmed, Soikat Hasan$$b12
001024854 7001_ $$aJoseph, George Vathakkattil$$b13
001024854 7001_ $$aLeto, Benedetto$$b14
001024854 7001_ $$aMicheli, Aurora$$b15
001024854 7001_ $$0P:(DE-Juel1)198673$$aMishra, Anurag Kumar$$b16
001024854 7001_ $$aLenz, Gregor$$b17
001024854 7001_ $$aSun, Tao$$b18
001024854 7001_ $$aAhmed, Zergham$$b19
001024854 7001_ $$aAkl, Mahmoud$$b20
001024854 7001_ $$aAnderson, Brian$$b21
001024854 7001_ $$aAndreou, Andreas G.$$b22
001024854 7001_ $$aBartolozzi, Chiara$$b23
001024854 7001_ $$aBasu, Arindam$$b24
001024854 7001_ $$aBogdan, Petrut$$b25
001024854 7001_ $$aBohte, Sander$$b26
001024854 7001_ $$aBuckley, Sonia$$b27
001024854 7001_ $$aCauwenberghs, Gert$$b28
001024854 7001_ $$aChicca, Elisabetta$$b29
001024854 7001_ $$aCorradi, Federico$$b30
001024854 7001_ $$ade Croon, Guido$$b31
001024854 7001_ $$aDanielescu, Andreea$$b32
001024854 7001_ $$aDaram, Anurag$$b33
001024854 7001_ $$aDavies, Mike$$b34
001024854 7001_ $$aDemirag, Yigit$$b35
001024854 7001_ $$aEshraghian, Jason$$b36
001024854 7001_ $$aFischer, Tobias$$b37
001024854 7001_ $$aForest, Jeremy$$b38
001024854 7001_ $$aFra, Vittorio$$b39
001024854 7001_ $$aFurber, Steve$$b40
001024854 7001_ $$aFurlong, P. Michael$$b41
001024854 7001_ $$aGilpin, William$$b42
001024854 7001_ $$aGilra, Aditya$$b43
001024854 7001_ $$aGonzalez, Hector A.$$b44
001024854 7001_ $$aIndiveri, Giacomo$$b45
001024854 7001_ $$aJoshi, Siddharth$$b46
001024854 7001_ $$aKaria, Vedant$$b47
001024854 7001_ $$aKhacef, Lyes$$b48
001024854 7001_ $$aKnight, James C.$$b49
001024854 7001_ $$aKriener, Laura$$b50
001024854 7001_ $$aKubendran, Rajkumar$$b51
001024854 7001_ $$aKudithipudi, Dhireesha$$b52
001024854 7001_ $$aLiu, Yao-Hong$$b53
001024854 7001_ $$aLiu, Shih-Chii$$b54
001024854 7001_ $$aMa, Haoyuan$$b55
001024854 7001_ $$aManohar, Rajit$$b56
001024854 7001_ $$aMargarit-Taulé, Josep Maria$$b57
001024854 7001_ $$aMayr, Christian$$b58
001024854 7001_ $$aMichmizos, Konstantinos$$b59
001024854 7001_ $$aMuir, Dylan$$b60
001024854 7001_ $$0P:(DE-Juel1)188273$$aNeftci, Emre$$b61
001024854 7001_ $$aNowotny, Thomas$$b62
001024854 7001_ $$aOttati, Fabrizio$$b63
001024854 7001_ $$aOzcelikkale, Ayca$$b64
001024854 7001_ $$aPanda, Priyadarshini$$b65
001024854 7001_ $$aPark, Jongkil$$b66
001024854 7001_ $$aPayvand, Melika$$b67
001024854 7001_ $$aPehle, Christian$$b68
001024854 7001_ $$aPetrovici, Mihai A.$$b69
001024854 7001_ $$aPierro, Alessandro$$b70
001024854 7001_ $$aPosch, Christoph$$b71
001024854 7001_ $$0P:(DE-Juel1)201426$$aRenner, Alpha$$b72
001024854 7001_ $$aSandamirskaya, Yulia$$b73
001024854 7001_ $$aSchaefer, Clemens JS$$b74
001024854 7001_ $$avan Schaik, André$$b75
001024854 7001_ $$aSchemmel, Johannes$$b76
001024854 7001_ $$aSchmidgall, Samuel$$b77
001024854 7001_ $$aSchuman, Catherine$$b78
001024854 7001_ $$aSeo, Jae-sun$$b79
001024854 7001_ $$aSheik, Sadique$$b80
001024854 7001_ $$aShrestha, Sumit Bam$$b81
001024854 7001_ $$aSifalakis, Manolis$$b82
001024854 7001_ $$aSironi, Amos$$b83
001024854 7001_ $$aStewart, Matthew$$b84
001024854 7001_ $$0P:(DE-Juel1)195754$$aStewart, Kenneth$$b85
001024854 7001_ $$aStewart, Terrence C.$$b86
001024854 7001_ $$aStratmann, Philipp$$b87
001024854 7001_ $$aTimcheck, Jonathan$$b88
001024854 7001_ $$aTömen, Nergis$$b89
001024854 7001_ $$aUrgese, Gianvito$$b90
001024854 7001_ $$aVerhelst, Marian$$b91
001024854 7001_ $$aVineyard, Craig M.$$b92
001024854 7001_ $$aVogginger, Bernhard$$b93
001024854 7001_ $$aYousefzadeh, Amirreza$$b94
001024854 7001_ $$aZohora, Fatima Tuz$$b95
001024854 7001_ $$aFrenkel, Charlotte$$b96
001024854 7001_ $$aReddi, Vijay Janapa$$b97
001024854 773__ $$a10.48550/ARXIV.2304.04640
001024854 8564_ $$uhttps://arxiv.org/abs/2304.04640
001024854 909CO $$ooai:juser.fz-juelich.de:1024854$$pVDB
001024854 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176778$$aForschungszentrum Jülich$$b3$$kFZJ
001024854 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)201205$$aForschungszentrum Jülich$$b4$$kFZJ
001024854 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)194363$$aForschungszentrum Jülich$$b12$$kFZJ
001024854 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)198673$$aForschungszentrum Jülich$$b16$$kFZJ
001024854 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188273$$aForschungszentrum Jülich$$b61$$kFZJ
001024854 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)201426$$aForschungszentrum Jülich$$b72$$kFZJ
001024854 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5234$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
001024854 9141_ $$y2024
001024854 920__ $$lyes
001024854 9201_ $$0I:(DE-Juel1)PGI-15-20210701$$kPGI-15$$lNeuromorphic Software Eco System$$x0
001024854 980__ $$apreprint
001024854 980__ $$aVDB
001024854 980__ $$aI:(DE-Juel1)PGI-15-20210701
001024854 980__ $$aUNRESTRICTED