Testability and dependability of AI hardware: Survey, trends, challenges, and perspectives
Hardware realization of artificial intelligence (AI) requires new design styles and even
underlying technologies than those used in traditional digital processors or logic circuits …
underlying technologies than those used in traditional digital processors or logic circuits …
Implementing spiking neural networks on neuromorphic architectures: A review
PK Huynh, ML Varshika, A Paul, M Isik, A Balaji… - ar** spiking neural networks to many-core neuromorphic hardware
The design of many-core neuromorphic hardware is becoming increasingly complex as
these systems are now expected to execute large machine-learning models. A predictable …
these systems are now expected to execute large machine-learning models. A predictable …
Design of many-core big little µBrains for energy-efficient embedded neuromorphic computing
As spiking-based deep learning inference applications are increasing in embedded
systems, these systems tend to integrate neuromorphic accelerators such as µBrain to …
systems, these systems tend to integrate neuromorphic accelerators such as µBrain to …
Research progress of neural synapses based on memristors
Y Li, K Su, H Chen, X Zou, C Wang, H Man, K Liu, X **… - Electronics, 2023 - mdpi.com
The memristor, characterized by its nano-size, nonvolatility, and continuously adjustable
resistance, is a promising candidate for constructing brain-inspired computing. It operates …
resistance, is a promising candidate for constructing brain-inspired computing. It operates …
Special session: Reliability analysis for AI/ML hardware
Artificial intelligence (AI) and Machine Learning (ML) are becoming pervasive in today's
applications, such as autonomous vehicles, healthcare, aerospace, cybersecurity, and many …
applications, such as autonomous vehicles, healthcare, aerospace, cybersecurity, and many …
Nonvolatile memories in spiking neural network architectures: Current and emerging trends
A sustainable computing scenario demands more energy-efficient processors.
Neuromorphic systems mimic biological functions by employing spiking neural networks for …
Neuromorphic systems mimic biological functions by employing spiking neural networks for …
NeuroXplorer 1.0: An extensible framework for architectural exploration with spiking neural networks
Recently, both industry and academia have proposed many different neuromorphic
architectures to execute applications that are designed with Spiking Neural Network (SNN) …
architectures to execute applications that are designed with Spiking Neural Network (SNN) …
On the role of system software in energy management of neuromorphic computing
Neuromorphic computing systems such as DYNAPs and Loihi have recently been
introduced to the computing community to improve performance and energy efficiency of …
introduced to the computing community to improve performance and energy efficiency of …