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001006790 1001_ $$0P:(DE-Juel1)174486$$aSiegel, Sebastian$$b0$$eCorresponding author
001006790 245__ $$aSystem model of neuromorphic sequence learning on a memristive crossbar array
001006790 260__ $$aBristol$$bIOP Publishing Ltd.$$c2023
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001006790 520__ $$aMachine learning models for sequence learning and processing often suffer from high energy consumption and require large amounts of training data. The brain presents more efficient solutions to how these types of tasks can be solved. While this has inspired the conception of novel brain-inspired algorithms, their realizations remain constrained to conventional von-Neumann machines. Therefore, the potential power efficiency of the algorithm cannot be exploited due to the inherent memory bottleneck of the computing architecture. Therefore, we present in this paper a dedicated hardware implementation of a biologically plausible version of the Temporal Memory component of the Hierarchical Temporal Memory concept. Our implementation is built on a memristive crossbar array and is the result of a hardware-algorithm co-design process. Rather than using the memristive devices solely for data storage, our approach leverages their specific switching dynamics to propose a formulation of the peripheral circuitry, resulting in a more efficient design. By combining a brain-like algorithm with emerging non-volatile memristive device technology we strive for maximum energy efficiency. We present simulation results on the training of complex high-order sequences and discuss how the system is able to predict in a context-dependent manner. Finally, we investigate the energy consumption during the training and conclude with a discussion of scaling prospects.
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001006790 536__ $$0G:(DE-82)BMBF-16ME0398K$$aBMBF 16ME0398K - Verbundprojekt: Neuro-inspirierte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft - NEUROTEC II - (BMBF-16ME0398K)$$cBMBF-16ME0398K$$x2
001006790 536__ $$0G:(DE-HGF)SO-092$$aACA - Advanced Computing Architectures (SO-092)$$cSO-092$$x3
001006790 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x4
001006790 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x5
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001006790 7001_ $$0P:(DE-Juel1)176778$$aBouhadjar, Younes$$b1
001006790 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b2
001006790 7001_ $$0P:(DE-Juel1)131022$$aWaser, R.$$b3
001006790 7001_ $$0P:(DE-Juel1)130620$$aDittmann, Regina$$b4$$ufzj
001006790 7001_ $$0P:(DE-HGF)0$$aWouters, Dirk$$b5
001006790 773__ $$0PERI:(DE-600)3099608-9$$a10.1088/2634-4386/acca45$$p024002$$tNeuromorphic computing and engineering$$v3$$x2634-4386$$y2023
001006790 8564_ $$uhttps://juser.fz-juelich.de/record/1006790/files/MemSpikingTM_Supplement_final.pdf$$yOpenAccess
001006790 8564_ $$uhttps://juser.fz-juelich.de/record/1006790/files/MemSpikingTM_final.pdf$$yOpenAccess
001006790 8564_ $$uhttps://juser.fz-juelich.de/record/1006790/files/Siegel_2023_Neuromorph._Comput._Eng._3_024002.pdf$$yOpenAccess
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001006790 9141_ $$y2023
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