to meet the extensive computational load presented by the rapidly growing Machine Learning (ML) and Artificial Intelligence (AI) algorithms such as Deep Neural Networks …
Processing-using-DRAM has been proposed for a limited set of basic operations (ie, logic operations, addition). However, in order to enable full adoption of processing-using-DRAM …
Advanced computing systems have long been enablers for breakthroughs in artificial intelligence (AI) and machine learning (ML) algorithms, either through sheer computational …
In this paper, we propose MRIMA, as a novel magnetic RAM (MRAM)-based in-memory accelerator for nonvolatile, flexible, and efficient in-memory computing. MRIMA transforms …
Y Pan, P Ouyang, Y Zhao, W Kang… - IEEE Transactions …, 2018 - ieeexplore.ieee.org
Due to additive operation's dominated computation and simplified network in binary convolutional neural network (BCNN), it is promising for Internet of Things scenarios which …
Data movement between main memory and the processor is a significant contributor to the execution time and energy consumption of memory-intensive applications. This data …
Data movement between the main memory and the processor is a key contributor to execution time and energy consumption in memory-intensive applications. This data …
QF Ou, BS Xiong, L Yu, J Wen, L Wang, Y Tong - Materials, 2020 - mdpi.com
Recent progress in the development of artificial intelligence technologies, aided by deep learning algorithms, has led to an unprecedented revolution in neuromorphic circuits …
S Umesh, S Mittal - Journal of Systems Architecture, 2019 - Elsevier
The rising overheads of data-movement and limitations of general-purpose processing architectures have led to a huge surge in the interest in “processing-in-memory”(PIM) …