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Multiple alignment of packet sequences for efficient communication in a many-core neuromorphic system: work-in-progress.

, , , , и . CASES, стр. 3:1-3:2. ACM, (2018)

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Francesco Bettella

Protein secondary structure prediction using optimized scoring functions: a comparative statistical method. FU Berlin, (2009)

Francesco Solimena

Francesco Pinosa

Francesco Knechtli

Francesco Virili

 

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Impact of graph partitioning on SNN placement for a multi-core neuromorphic architecture: work-in-progress., , , и . CASES, стр. 4:1-4:2. ACM, (2018)Toolchain integration of runtime variability and aging awareness in multicore platforms., , , и . FDL, стр. 1-8. IEEE, (2016)Directly-trained Spiking Neural Networks for Deep Reinforcement Learning: Energy efficient implementation of event-based obstacle avoidance on a neuromorphic accelerator., , , , , и . Neurocomputing, (декабря 2023)RISC-V Processor Technologies for Aerospace Applications in the ISOLDE Project., , , , , , , , , и 13 other автор(ы). SAMOS, том 14385 из Lecture Notes in Computer Science, стр. 363-378. Springer, (2023)Monte Cimone: Paving the Road for the First Generation of RISC-V High-Performance Computers., , , , , , , , , и 2 other автор(ы). SOCC, стр. 1-6. IEEE, (2022)Mapping Spiking Neural Networks on Multi-core Neuromorphic Platforms: Problem Formulation and Performance Analysis., , , и . VLSI-SoC (Selected Papers), том 561 из IFIP Advances in Information and Communication Technology, стр. 167-186. Springer, (2018)Making the Most of Scarce Input Data in Deep Learning-Based Source Code Classification for Heterogeneous Device Mapping., , , и . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 41 (6): 1636-1648 (2022)Optimizing Network Traffic for Spiking Neural Network Simulations on Densely Interconnected Many-Core Neuromorphic Platforms., , , и . IEEE Trans. Emerg. Top. Comput., 6 (3): 317-329 (2018)Top-Down Profiling of Application Specific Many-core Neuromorphic Platforms., , и . MCSoC, стр. 127-134. IEEE Computer Society, (2015)AutoGrAN: Autonomous Vehicle LiDAR Contaminant Detection using Graph Attention Networks., , , , , , и . ICPE (Companion), стр. 112-119. ACM, (2024)