A review of near-memory computing architectures: Opportunities and challenges

G Singh, L Chelini, S Corda, AJ Awan… - 2018 21st Euromicro …, 2018 - ieeexplore.ieee.org
The conventional approach of moving stored data to the CPU for computation has become a
major performance bottleneck for emerging scale-out data-intensive applications due to their …

A modern primer on processing in memory

O Mutlu, S Ghose, J Gómez-Luna… - … computing: from devices …, 2022 - Springer
Modern computing systems are overwhelmingly designed to move data to computation. This
design choice goes directly against at least three key trends in computing that cause …

The landscape of exascale research: A data-driven literature analysis

S Heldens, P Hijma, BV Werkhoven… - ACM Computing …, 2020 - dl.acm.org
The next generation of supercomputers will break the exascale barrier. Soon we will have
systems capable of at least one quintillion (billion billion) floating-point operations per …

Pipelayer: A pipelined reram-based accelerator for deep learning

L Song, X Qian, H Li, Y Chen - 2017 IEEE international …, 2017 - ieeexplore.ieee.org
Convolution neural networks (CNNs) are the heart of deep learning applications. Recent
works PRIME [1] and ISAAC [2] demonstrated the promise of using resistive random access …

Prime: A novel processing-in-memory architecture for neural network computation in reram-based main memory

P Chi, S Li, C Xu, T Zhang, J Zhao, Y Liu… - ACM SIGARCH …, 2016 - dl.acm.org
Processing-in-memory (PIM) is a promising solution to address the" memory wall"
challenges for future computer systems. Prior proposed PIM architectures put additional …

Benchmarking a new paradigm: Experimental analysis and characterization of a real processing-in-memory system

J Gómez-Luna, I El Hajj, I Fernandez… - IEEE …, 2022 - ieeexplore.ieee.org
Many modern workloads, such as neural networks, databases, and graph processing, are
fundamentally memory-bound. For such workloads, the data movement between main …

Drisa: A dram-based reconfigurable in-situ accelerator

S Li, D Niu, KT Malladi, H Zheng, B Brennan… - Proceedings of the 50th …, 2017 - dl.acm.org
Data movement between the processing units and the memory in traditional von Neumann
architecture is creating the" memory wall" problem. To bridge the gap, two approaches, the …

Pinatubo: A processing-in-memory architecture for bulk bitwise operations in emerging non-volatile memories

S Li, C Xu, Q Zou, J Zhao, Y Lu, Y Xie - Proceedings of the 53rd Annual …, 2016 - dl.acm.org
Processing-in-memory (PIM) provides high bandwidth, massive parallelism, and high
energy efficiency by implementing computations in main memory, therefore eliminating the …

Newton: A DRAM-maker's accelerator-in-memory (AiM) architecture for machine learning

M He, C Song, I Kim, C Jeong, S Kim… - 2020 53rd Annual …, 2020 - ieeexplore.ieee.org
Advances in machine learning (ML) have ignited hardware innovations for efficient
execution of the ML models many of which are memory-bound (eg, long short-term …

Computing in memory with spin-transfer torque magnetic RAM

S Jain, A Ranjan, K Roy… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
In-memory computing is a promising approach to addressing the processor-memory data
transfer bottleneck in computing systems. We propose spin-transfer torque compute-in …