An Overview of In-memory Processing with Emerging Non-volatile Memory for Data-intensive Applications
The conventional von Neumann architecture has been revealed as a major performance and energy bottleneck for rising data-intensive applications. %, due to the intensive data movements. The decade-old idea of leveraging in-memory processing to eliminate substantial data movements has returned and led extensive research activities. The effectiveness of in-memory processing heavily relies on memory scalability, which cannot be satisfied by traditional memory technologies. Emerging non-volatile memories (eNVMs) that pose appealing qualities such as excellent scaling and low energy consumption, on the other hand, have been heavily investigated and explored for realizing in-memory processing architecture. In this paper, we summarize the recent research progress in eNVM-based in-memory processing from various aspects, including the adopted memory technologies, locations of the in-memory processing in the system, supported arithmetics, as well as applied applications.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Device and System Level Design Considerations for Analog-Non-Volatile-Memory Based Neuromorphic Architectures
This paper gives an overview of recent progress in the brain inspired computing field with a focus on implementation using emerging memories as electronic synapses. Design considerations and challenges such as requiremen…
Neuron inspired data encoding memristive multi-level memory cell
Mapping neuro-inspired algorithms to sensor backplanes of on-chip hardware require shifting the signal processing from digital to the analog domain, demanding memory technologies beyond conventional CMOS binary storage u…
Low-Rank Training of Deep Neural Networks for Emerging Memory Technology
The recent success of neural networks for solving difficult decision tasks has incentivized incorporating smart decision making "at the edge." However, this work has traditionally focused on neural network inference, rat…
Computational EfficiencyDecision MakingFederated LearningLow Rank Training of Deep Neural Networks for Emerging Memory Technology
The recent success of neural networks for solving difficult decision tasks has incentivized incorporating smart decision making "at the edge." However, this work has traditionally focused on neural network inference, rat…
Computational EfficiencyDecision MakingFederated LearningQuantizationA Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms
Recent trends in deep learning (DL) have made hardware accelerators essential for various high-performance computing (HPC) applications, including image classification, computer vision, and speech recognition. This surve…
Deep LearningGPUimage-classificationImage Classification+3