Affiliation:
1. Department of Computer Technologies, Torul Vocational School Gümüşhane University Gümüşhane Turkey
2. Faculty of Engineering and Architecture, Electrical and Electronics Engineering Recep Tayyip Erdoğan University Rize Turkey
3. Faculty of Engineering, Computer Engineering Atatürk University Erzurum Turkey
Abstract
AbstractThe prevalence of artificial intelligence applications using artificial neural network architectures for functions such as natural language processing, text prediction, object detection, speech, and image recognition has significantly increased in today's world. The computational functions performed by artificial neural networks in classical applications require intensive and large‐scale data movement between memory and processing units. Various software and hardware efforts are being made to perform these operations more efficiently. Despite these efforts, latency in data traffic and the substantial amount of energy consumed in data processing emerge as bottleneck disadvantages of the Von Neumann architecture. To overcome this bottleneck problem, it is necessary to develop hardware units specific to artificial intelligence applications. For this purpose, neuro‐inspired computing chips are believed to provide an effective approach by designing and integrating a set of features inspired by neurobiological systems at the hardware level to address the problems arising in artificial intelligence applications. The most notable among these approaches is memristor‐based neuromorphic computing systems. Memristors are seen as promising devices for hardware‐level improvement in terms of speed and energy because they possess non‐volatile memory and exhibit analog behavior. They enable effective storage and processing of synaptic weights, offering solutions for hardware‐level development. Taking into account these advantages of memristors, this study examines the research conducted on artificial neural networks and hardware that can directly perform deep learning functions and mimic the biological brain, which is different from classical systems in today's context.
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1 articles.
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1. Research on Fault-Tolerant Algorithm for Memristor Neural Networks Based on On-Chip Training;2024 9th International Conference on Electronic Technology and Information Science (ICETIS);2024-05-17