Examining the robustness of spiking neural networks on non-ideal memristive crossbars
Spiking Neural Networks (SNNs) have recently emerged as the low-power alternative to
Artificial Neural Networks (ANNs) owing to their asynchronous, sparse, and binary …
Artificial Neural Networks (ANNs) owing to their asynchronous, sparse, and binary …
Three challenges in reram-based process-in-memory for neural network
Z Yang, K Liu, Y Duan, M Fan… - 2023 IEEE 5th …, 2023 - ieeexplore.ieee.org
Artificial intelligence (AI) has been successfully applied to various fields of natural science.
One of the biggest challenges in AI acceleration is the performance and energy bottleneck …
One of the biggest challenges in AI acceleration is the performance and energy bottleneck …
Efficient and Reliable Vector Similarity Search Using Asymmetric Encoding with NAND-Flash for Many-Class Few-Shot Learning
While memory-augmented neural networks (MANNs) offer an effective solution for few-shot
learning (FSL) by integrating deep neural networks with external memory, the capacity …
learning (FSL) by integrating deep neural networks with external memory, the capacity …
Mitigating Non-ideality Issues of Analog Computing-In-Memory in DNN-based designs
Analog Computing-in-memory (ACIM) has emerged as a promising approach to enhance
energy efficiency and throughput in matrix-vector multiplications (MVMs) for deep neural …
energy efficiency and throughput in matrix-vector multiplications (MVMs) for deep neural …