Spiking Neural Networks Hardware Implementations and Challenges: a Survey
| Autor: | Thomas Mesquida, Marina Reyboz, Elisa Vianello, Francois Rummens, Alexandre Valentian, Maxence Bouvier, Edith Beigne |
|---|---|
| Přispěvatelé: | Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019) |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology 01 natural sciences Field (computer science) [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] symbols.namesake 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Leverage (statistics) Neural and Evolutionary Computing (cs.NE) Electrical and Electronic Engineering 010302 applied physics Spiking neural network Artificial neural network business.industry Deep learning Computer Science - Neural and Evolutionary Computing Computer architecture Neuromorphic engineering Hardware and Architecture symbols 020201 artificial intelligence & image processing Artificial intelligence State (computer science) business Software Von Neumann architecture |
| Zdroj: | ACM Journal on Emerging Technologies in Computing Systems ACM Journal on Emerging Technologies in Computing Systems, Association for Computing Machinery, 2019, 15 (2), pp.1-35. ⟨10.1145/3304103⟩ ACM Journal on Emerging Technologies in Computing Systems, 2019, 15 (2), pp.1-35. ⟨10.1145/3304103⟩ |
| ISSN: | 1550-4832 1550-4840 |
| DOI: | 10.48550/arxiv.2005.01467 |
| Popis: | Neuromorphic computing is henceforth a major research field for both academic and industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at bringing closer the memory and the computational elements to efficiently evaluate machine-learning algorithms. Recently, Spiking Neural Networks, a generation of cognitive algorithms employing computational primitives mimicking neuron and synapse operational principles, have become an important part of deep learning. They are expected to improve the computational performance and efficiency of neural networks, but are best suited for hardware able to support their temporal dynamics. In this survey, we present the state of the art of hardware implementations of spiking neural networks and the current trends in algorithm elaboration from model selection to training mechanisms. The scope of existing solutions is extensive; we thus present the general framework and study on a case-by-case basis the relevant particularities. We describe the strategies employed to leverage the characteristics of these event-driven algorithms at the hardware level and discuss their related advantages and challenges. Comment: Pre-print version of the file authorized for publication |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|